The Trusted MLOps Stack: Open Source Tools for Reproducible AI with Explanations

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Open Source Research
Open Source Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.20359688  52stabilfr·wdophcgmx
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[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (62 × 60%) + Required (2/5 × 30%) + Optional (1/4 × 10%)

Explainability in artificial intelligence remains a critical barrier to adoption in safety‑critical domains such as healthcare, finance, and autonomous systems. While many commercial platforms tout built‑in interpretability, they often lock users into proprietary ecosystems and obscure the underlying model internals. This article presents a fully open source stack that enables reproducible, aud...

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Open Source Research by Oleh Ivchenko DOI: 10.5281/zenodo.20359688 52stabilfr·wdophcgmx
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[s]Reviewed Sources4%○≥80% from editorially reviewed sources
[t]Trusted78%○≥80% from verified, high-quality sources
[a]DOI71%○≥80% have a Digital Object Identifier
[b]CrossRef4%○≥80% indexed in CrossRef
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[l]Academic75%○≥80% from journals/conferences/preprints
[f]Free Access84%✓≥80% are freely accessible
[r]References51 refs✓Minimum 10 references required
[w]Words [REQ]1,029✗Minimum 2,000 words for a full research article. Current: 1,029
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.20359688
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]55%✗≥60% of references from 2025–2026. Current: 55%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams2✓Mermaid architecture/flow diagrams. Current: 2
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (62 × 60%) + Required (2/5 × 30%) + Optional (1/4 × 10%)
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The Transformation of Shadow Labor Markets: How AI Platforms Reshape Informal Work

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Economic Research by Oleh Ivchenko, Iryna Ivchenko & Dmytro Grybeniuk  ·  DOI: 10.5281/zenodo.20358396  42stabilfr·wdophcgmx
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[t]Trusted75%○≥80% from verified, high-quality sources
[a]DOI38%○≥80% have a Digital Object Identifier
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[w]Words [REQ]875✗Minimum 2,000 words for a full research article. Current: 875
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[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]20%✗≥60% of references from 2025–2026. Current: 20%
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[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (46 × 60%) + Required (2/5 × 30%) + Optional (1/4 × 10%)

The rise of AI-driven gig platforms has dramatically altered informal labor ecosystems, creating new shadow market dynamics that traditional economic models fail to capture. This article investigates how platform design choices directly reshape worker vulnerability, income stability, and social protection gaps in emerging economies. We demonstrate that platform-mediated work arrangements are no...

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Economic Research by Oleh Ivchenko, Iryna Ivchenko & Dmytro Grybeniuk DOI: 10.5281/zenodo.20358396 42stabilfr·wdophcgmx
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[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted75%○≥80% from verified, high-quality sources
[a]DOI38%○≥80% have a Digital Object Identifier
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[l]Academic50%○≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
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[w]Words [REQ]875✗Minimum 2,000 words for a full research article. Current: 875
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.20358396
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]20%✗≥60% of references from 2025–2026. Current: 20%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
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[m]Diagrams2✓Mermaid architecture/flow diagrams. Current: 2
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (46 × 60%) + Required (2/5 × 30%) + Optional (1/4 × 10%)
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Public Procurement AI: Detecting Corruption Patterns with Explainable Machine Learning

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Economic Research
Economic Research by Oleh Ivchenko, Iryna Ivchenko & Dmytro Grybeniuk  ·  DOI: 10.5281/zenodo.20351356  64stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources44%○≥80% from editorially reviewed sources
[t]Trusted78%○≥80% from verified, high-quality sources
[a]DOI67%○≥80% have a Digital Object Identifier
[b]CrossRef56%○≥80% indexed in CrossRef
[i]Indexed56%○≥80% have metadata indexed
[l]Academic78%○≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
[r]References9 refs○Minimum 10 references required
[w]Words [REQ]1,198✗Minimum 2,000 words for a full research article. Current: 1,198
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.20351356
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]63%✓≥60% of references from 2025–2026. Current: 63%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams3✓Mermaid architecture/flow diagrams. Current: 3
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (73 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)

Government procurement processes are vulnerable to corruption, inefficiency, and opaque decision‑making. This article presents an explainable artificial intelligence (XAI) framework for detecting corruption patterns in public procurement datasets, focusing on invoice analysis and contract anomaly detection. Using a combination of statistical feature extraction, graph‑based anomaly scoring, and ...

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Economic Research by Oleh Ivchenko, Iryna Ivchenko & Dmytro Grybeniuk DOI: 10.5281/zenodo.20351356 64stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources44%○≥80% from editorially reviewed sources
[t]Trusted78%○≥80% from verified, high-quality sources
[a]DOI67%○≥80% have a Digital Object Identifier
[b]CrossRef56%○≥80% indexed in CrossRef
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[l]Academic78%○≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
[r]References9 refs○Minimum 10 references required
[w]Words [REQ]1,198✗Minimum 2,000 words for a full research article. Current: 1,198
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.20351356
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]63%✓≥60% of references from 2025–2026. Current: 63%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams3✓Mermaid architecture/flow diagrams. Current: 3
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (73 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)
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Post-Deployment XAI Monitoring: Specification Requirements for Explanation Drift Detection

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Academic Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.20347195  31stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted50%○≥80% from verified, high-quality sources
[a]DOI25%○≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed0%○≥80% have metadata indexed
[l]Academic50%○≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
[r]References4 refs○Minimum 10 references required
[w]Words [REQ]1,320✗Minimum 2,000 words for a full research article. Current: 1,320
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.20347195
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]33%✗≥60% of references from 2025–2026. Current: 33%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams0○Mermaid architecture/flow diagrams. Current: 0
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (31 × 60%) + Required (2/5 × 30%) + Optional (0/4 × 10%)

Post-deployment monitoring of explainable AI (XAI) systems has emerged as a critical concern for maintaining trustworthy AI behaviors over time [1]. While pre-deployment validation establishes baseline explanation quality, it does not guarantee sustained performance when models encounter distribution shifts, concept drift, or evolving user expectations [2]. This article addresses the research g...

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Academic Research by Oleh Ivchenko DOI: 10.5281/zenodo.20347195 31stabilfr·wdophcgmx
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[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted50%○≥80% from verified, high-quality sources
[a]DOI25%○≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed0%○≥80% have metadata indexed
[l]Academic50%○≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
[r]References4 refs○Minimum 10 references required
[w]Words [REQ]1,320✗Minimum 2,000 words for a full research article. Current: 1,320
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.20347195
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]33%✗≥60% of references from 2025–2026. Current: 33%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams0○Mermaid architecture/flow diagrams. Current: 0
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (31 × 60%) + Required (2/5 × 30%) + Optional (0/4 × 10%)
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The Education AI Transformation: From Classrooms to Personalized Learning Pathways

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Journal Commentary
Journal Commentary by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.20337245  34stabilfr·wdophcgmx
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[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted60%○≥80% from verified, high-quality sources
[a]DOI20%○≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed0%○≥80% have metadata indexed
[l]Academic40%○≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
[r]References5 refs○Minimum 10 references required
[w]Words [REQ]1,507✗Minimum 2,000 words for a full research article. Current: 1,507
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.20337245
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]33%✗≥60% of references from 2025–2026. Current: 33%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams2✓Mermaid architecture/flow diagrams. Current: 2
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (32 × 60%) + Required (2/5 × 30%) + Optional (1/4 × 10%)

The integration of artificial intelligence (AI) into educational environments is reshaping how l[REDACTED]g is delivered, assessed, and accessed. Recent advances in adaptive l[REDACTED]g systems, automated grading, and AI-driven analytics promise significant improvements in personalization, efficiency, and equity. However, the extent to which these technologies can universally transform educati...

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Journal Commentary by Oleh Ivchenko DOI: 10.5281/zenodo.20337245 34stabilfr·wdophcgmx
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[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted60%○≥80% from verified, high-quality sources
[a]DOI20%○≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed0%○≥80% have metadata indexed
[l]Academic40%○≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
[r]References5 refs○Minimum 10 references required
[w]Words [REQ]1,507✗Minimum 2,000 words for a full research article. Current: 1,507
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.20337245
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]33%✗≥60% of references from 2025–2026. Current: 33%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams2✓Mermaid architecture/flow diagrams. Current: 2
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (32 × 60%) + Required (2/5 × 30%) + Optional (1/4 × 10%)
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Reproducibility in XAI Research: Open Source Benchmarks for Explanation Quality

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Open Source Research
Open Source Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.20318088  63stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted96%✓≥80% from verified, high-quality sources
[a]DOI89%✓≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed4%○≥80% have metadata indexed
[l]Academic93%✓≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
[r]References27 refs✓Minimum 10 references required
[w]Words [REQ]1,257✗Minimum 2,000 words for a full research article. Current: 1,257
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.20318088
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]81%✓≥60% of references from 2025–2026. Current: 81%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams2✓Mermaid architecture/flow diagrams. Current: 2
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (71 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)

Accurate and reproducible evaluation of explanation fidelity is essential for advancing XAI research. While several metrics have been proposed, no standardized benchmark framework exists that enables systematic comparison across methods. This article presents an open-source benchmark suite designed to assess explanation quality across multiple XAI techniques. Drawing on recent literature [1], w...

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Open Source Research by Oleh Ivchenko DOI: 10.5281/zenodo.20318088 63stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted96%✓≥80% from verified, high-quality sources
[a]DOI89%✓≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed4%○≥80% have metadata indexed
[l]Academic93%✓≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
[r]References27 refs✓Minimum 10 references required
[w]Words [REQ]1,257✗Minimum 2,000 words for a full research article. Current: 1,257
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.20318088
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]81%✓≥60% of references from 2025–2026. Current: 81%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams2✓Mermaid architecture/flow diagrams. Current: 2
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (71 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)
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Digital Transformation Economics: When AI Adoption Reduces Informality

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Economic Research
Economic Research by Oleh Ivchenko, Iryna Ivchenko & Dmytro Grybeniuk  ·  DOI: 10.5281/zenodo.20312094  39stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted67%○≥80% from verified, high-quality sources
[a]DOI33%○≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed0%○≥80% have metadata indexed
[l]Academic67%○≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
[r]References3 refs○Minimum 10 references required
[w]Words [REQ]310✗Minimum 2,000 words for a full research article. Current: 310
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.20312094
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]33%✗≥60% of references from 2025–2026. Current: 33%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams2✓Mermaid architecture/flow diagrams. Current: 2
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (40 × 60%) + Required (2/5 × 30%) + Optional (1/4 × 10%)

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Economic Research by Oleh Ivchenko, Iryna Ivchenko & Dmytro Grybeniuk DOI: 10.5281/zenodo.20312094 39stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted67%○≥80% from verified, high-quality sources
[a]DOI33%○≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed0%○≥80% have metadata indexed
[l]Academic67%○≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
[r]References3 refs○Minimum 10 references required
[w]Words [REQ]310✗Minimum 2,000 words for a full research article. Current: 310
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.20312094
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]33%✗≥60% of references from 2025–2026. Current: 33%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams2✓Mermaid architecture/flow diagrams. Current: 2
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (40 × 60%) + Required (2/5 × 30%) + Optional (1/4 × 10%)
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Human-Readable AI Explanations: Specification for Audience-Appropriate Transparency

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Academic Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.20303709  70stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources60%○≥80% from editorially reviewed sources
[t]Trusted87%✓≥80% from verified, high-quality sources
[a]DOI80%✓≥80% have a Digital Object Identifier
[b]CrossRef60%○≥80% indexed in CrossRef
[i]Indexed60%○≥80% have metadata indexed
[l]Academic87%✓≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
[r]References15 refs✓Minimum 10 references required
[w]Words [REQ]698✗Minimum 2,000 words for a full research article. Current: 698
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.20303709
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]64%✓≥60% of references from 2025–2026. Current: 64%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams1✓Mermaid architecture/flow diagrams. Current: 1
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (82 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)

The proliferation of artificial intelligence systems has foregrounded the need for explanations that are not only technically accurate but also tailored to the cognitive and professional contexts of diverse stakeholders. This article establishes a systematic specification framework for generating audience‑appropriate explanations of AI decisions, bridging the gap between model‑level transparenc...

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Academic Research by Oleh Ivchenko DOI: 10.5281/zenodo.20303709 70stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources60%○≥80% from editorially reviewed sources
[t]Trusted87%✓≥80% from verified, high-quality sources
[a]DOI80%✓≥80% have a Digital Object Identifier
[b]CrossRef60%○≥80% indexed in CrossRef
[i]Indexed60%○≥80% have metadata indexed
[l]Academic87%✓≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
[r]References15 refs✓Minimum 10 references required
[w]Words [REQ]698✗Minimum 2,000 words for a full research article. Current: 698
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.20303709
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]64%✓≥60% of references from 2025–2026. Current: 64%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams1✓Mermaid architecture/flow diagrams. Current: 1
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (82 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)
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The Transportation AI Transformation: From Vehicles to Logistics Networks

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Journal Commentary
Journal Commentary by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.20299462  33stabilfr·wdophcgmx
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[t]Trusted50%○≥80% from verified, high-quality sources
[a]DOI25%○≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed0%○≥80% have metadata indexed
[l]Academic50%○≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
[r]References4 refs○Minimum 10 references required
[w]Words [REQ]1,686✗Minimum 2,000 words for a full research article. Current: 1,686
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.20299462
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]33%✗≥60% of references from 2025–2026. Current: 33%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams1✓Mermaid architecture/flow diagrams. Current: 1
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (31 × 60%) + Required (2/5 × 30%) + Optional (1/4 × 10%)

The logistics sector stands at a pivotal juncture where artificial intelligence transitions from isolated applications in autonomous vehicles to integrated, network‑wide solutions that reconfigure route optimization, fleet management, and supply chain coordination [1]. This article synthesizes recent empirical findings, technological advancements, and emerging best practices to articulate a com...

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Journal Commentary by Oleh Ivchenko DOI: 10.5281/zenodo.20299462 33stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted50%○≥80% from verified, high-quality sources
[a]DOI25%○≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed0%○≥80% have metadata indexed
[l]Academic50%○≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
[r]References4 refs○Minimum 10 references required
[w]Words [REQ]1,686✗Minimum 2,000 words for a full research article. Current: 1,686
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.20299462
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]33%✗≥60% of references from 2025–2026. Current: 33%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams1✓Mermaid architecture/flow diagrams. Current: 1
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (31 × 60%) + Required (2/5 × 30%) + Optional (1/4 × 10%)
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Supply Chain Security in Open Source AI: Auditing XAI Tool Dependencies

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Open Source Research
Open Source Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.20274804  55stabilfr·wdophcgmx
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[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted94%✓≥80% from verified, high-quality sources
[a]DOI6%○≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed0%○≥80% have metadata indexed
[l]Academic91%✓≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
[r]References32 refs✓Minimum 10 references required
[w]Words [REQ]2,769✓Minimum 2,000 words for a full research article. Current: 2,769
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.20274804
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]93%✓≥60% of references from 2025–2026. Current: 93%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams2✓Mermaid architecture/flow diagrams. Current: 2
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (48 × 60%) + Required (4/5 × 30%) + Optional (1/4 × 10%)

The rapid adoption of explainable artificial intelligence (XAI) tools within open sourceMachine L[REDACTED]g (ML) ecosystems has amplified concerns regarding supply chain security. While XAI techniques enhance model transparency, their integration often relies on third‑party libraries, data pipelines, and inference services that introduce hidden vulnerabilities. This article investigates the se...

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Open Source Research by Oleh Ivchenko DOI: 10.5281/zenodo.20274804 55stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted94%✓≥80% from verified, high-quality sources
[a]DOI6%○≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed0%○≥80% have metadata indexed
[l]Academic91%✓≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
[r]References32 refs✓Minimum 10 references required
[w]Words [REQ]2,769✓Minimum 2,000 words for a full research article. Current: 2,769
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.20274804
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]93%✓≥60% of references from 2025–2026. Current: 93%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams2✓Mermaid architecture/flow diagrams. Current: 2
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (48 × 60%) + Required (4/5 × 30%) + Optional (1/4 × 10%)
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