Stabilarity Research Platform Is Now Open — Free API Access for All Researchers

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DOI: 10.5281/zenodo.18928330  56stabilfr·wdophcgmx
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Score = Ref Trust (69 × 60%) + Required (2/5 × 30%) + Optional (1/4 × 10%)

This paper presents the Stabilarity Research Platform — an open, API-accessible research infrastructure exposing validated machine learning models, geopolitical risk datasets, and decision optimization tools to the global research community at no cost. The platform implements FAIR data principles (Wilkinson et al., 2016), providing composable, versioned endpoints for: (1) medical imaging classi...

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DOI: 10.5281/zenodo.18928330 56stabilfr·wdophcgmx
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[s]Reviewed Sources35%○≥80% from editorially reviewed sources
[t]Trusted94%✓≥80% from verified, high-quality sources
[a]DOI41%○≥80% have a Digital Object Identifier
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[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
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[h]Freshness [REQ]13%✗≥60% of references from 2025–2026. Current: 13%
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[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (69 × 60%) + Required (2/5 × 30%) + Optional (1/4 × 10%)
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Carbon Ledger of AI Inference: Accounting for Emissions Across Distributed Computing Nodes

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AI Economics
AI Economics by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.22181262  39stabilfr·wdophcgmx
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Score = Ref Trust (40 × 60%) + Required (2/5 × 30%) + Optional (1/4 × 10%)

The rapid expansion of AI workloads across distributed computing infrastructures has precipitated a critical environmental externality: uncontrolled carbon emissions from energy-intensive inference operations. This article designs a transparent ledger framework to systematically track, quantify, and mitigate carbon footprints of AI workloads spanning the full lifecycle from training through dep...

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AI Economics by Oleh Ivchenko DOI: 10.5281/zenodo.22181262 39stabilfr·wdophcgmx
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[t]Trusted60%○≥80% from verified, high-quality sources
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Score = Ref Trust (40 × 60%) + Required (2/5 × 30%) + Optional (1/4 × 10%)
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Hybrid Warfare Signal Intelligence: AI for Detecting Coordinated Disinformation in Critical Infrastructure

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Geopolitical Research
Geopolitical Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.22164310  27stabilfr·wdophcgmx
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[t]Trusted33%○≥80% from verified, high-quality sources
[a]DOI33%○≥80% have a Digital Object Identifier
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[w]Words [REQ]1✗Minimum 2,000 words for a full research article. Current: 1
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.22164310
[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
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[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (25 × 60%) + Required (2/5 × 30%) + Optional (0/4 × 10%)

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Geopolitical Research by Oleh Ivchenko DOI: 10.5281/zenodo.22164310 27stabilfr·wdophcgmx
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[t]Trusted33%○≥80% from verified, high-quality sources
[a]DOI33%○≥80% have a Digital Object Identifier
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[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
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[h]Freshness [REQ]33%✗≥60% of references from 2025–2026. Current: 33%
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Semantic Coherence vs Plagiarism Thresholds: Automated Detection of Overlaps in AI-Written Content

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Quality Research
Quality Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.22157306  27stabilfr·wdophcgmx
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[t]Trusted33%○≥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]Academic33%○≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
[r]References3 refs○Minimum 10 references required
[w]Words [REQ]1✗Minimum 2,000 words for a full research article. Current: 1
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.22157306
[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 (25 × 60%) + Required (2/5 × 30%) + Optional (0/4 × 10%)

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Quality Research by Oleh Ivchenko DOI: 10.5281/zenodo.22157306 27stabilfr·wdophcgmx
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[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted33%○≥80% from verified, high-quality sources
[a]DOI33%○≥80% have a Digital Object Identifier
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[f]Free Access100%✓≥80% are freely accessible
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[w]Words [REQ]1✗Minimum 2,000 words for a full research article. Current: 1
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[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]33%✗≥60% of references from 2025–2026. Current: 33%
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[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (25 × 60%) + Required (2/5 × 30%) + Optional (0/4 × 10%)
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AI Alignment Progress Report 2025: RLHF Successors and Constitutional AI Evaluation

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Journal Commentary
Journal Commentary by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.22149662  31stabilfr·wdophcgmx
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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]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%)

Academic Citation: Ivchenko, Oleh (2026). AI Alignment Progress Report 2025: RLHF Successors and Constitutional AI Evaluation. Research article: AI Alignment Progress Report 2025: RLHF Successors and Constitutional AI Evaluation. Odessa National Polytechnic University, Department of Economic Cybernetics. DOI: 10.5281/zenodo.22149662  ·  View on Zenodo (CERN)

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Journal Commentary by Oleh Ivchenko DOI: 10.5281/zenodo.22149662 31stabilfr·wdophcgmx
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[w]Words [REQ]52✗Minimum 2,000 words for a full research article. Current: 52
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.22149662
[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
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[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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Spec-Driven Development with AI Agents: A Brownfield Walkthrough

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Academic Research
Academic Research by Oleh Ivchenko  21stabilfr·wdophcgmx
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[t]Trusted29%○≥80% from verified, high-quality sources
[a]DOI0%○≥80% have a Digital Object Identifier
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[l]Academic14%○≥80% from journals/conferences/preprints
[f]Free Access57%○≥80% are freely accessible
[r]References7 refs○Minimum 10 references required
[w]Words [REQ]5,458✓Minimum 2,000 words for a full research article. Current: 5,458
[d]DOI [REQ]✗✗Zenodo DOI registered for persistent citation
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]0%✗≥60% of references from 2025–2026. Current: 0%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
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[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (15 × 60%) + Required (2/5 × 30%) + Optional (0/4 × 10%)

A full field guide from a live CoffeeJUG session: spec-driven development with OpenSpec on a brownfield codebase — history, method, tool internals, a full annotated walkthrough, and the practices that only show up after a few real changes.

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Academic Research by Oleh Ivchenko 21stabilfr·wdophcgmx
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[t]Trusted29%○≥80% from verified, high-quality sources
[a]DOI0%○≥80% have a Digital Object Identifier
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[w]Words [REQ]5,458✓Minimum 2,000 words for a full research article. Current: 5,458
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[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
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[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (15 × 60%) + Required (2/5 × 30%) + Optional (0/4 × 10%)
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Labor Market Informality Dynamics: AI Forecasting of Shadow Employment Under Economic Shocks

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Economic Research
Economic Research by Oleh Ivchenko, Iryna Ivchenko & Dmytro Grybeniuk  ·  DOI: 10.5281/zenodo.22135534  75stabilfr·wdophcgmx
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[t]Trusted88%✓≥80% from verified, high-quality sources
[a]DOI76%○≥80% have a Digital Object Identifier
[b]CrossRef59%○≥80% indexed in CrossRef
[i]Indexed59%○≥80% have metadata indexed
[l]Academic82%✓≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
[r]References17 refs✓Minimum 10 references required
[w]Words [REQ]4,417✓Minimum 2,000 words for a full research article. Current: 4,417
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.22135534
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]73%✓≥60% of references from 2025–2026. Current: 73%
[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 (80 × 60%) + Required (4/5 × 30%) + Optional (1/4 × 10%)

This article investigates the application of machine l[REDACTED]g (ML) forecasting models to estimate informal employment levels under economic stress scenarios. We calibrate our models using COVID-19 pandemic data and apply them to predict informality responses to AI-driven structural unemployment. Our research addresses three key questions: (RQ1) How accurately can ML models forecast informal...

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Economic Research by Oleh Ivchenko, Iryna Ivchenko & Dmytro Grybeniuk DOI: 10.5281/zenodo.22135534 75stabilfr·wdophcgmx
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[s]Reviewed Sources53%○≥80% from editorially reviewed sources
[t]Trusted88%✓≥80% from verified, high-quality sources
[a]DOI76%○≥80% have a Digital Object Identifier
[b]CrossRef59%○≥80% indexed in CrossRef
[i]Indexed59%○≥80% have metadata indexed
[l]Academic82%✓≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
[r]References17 refs✓Minimum 10 references required
[w]Words [REQ]4,417✓Minimum 2,000 words for a full research article. Current: 4,417
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.22135534
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]73%✓≥60% of references from 2025–2026. Current: 73%
[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 (80 × 60%) + Required (4/5 × 30%) + Optional (1/4 × 10%)
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AI Regression Testing: Detecting Behavioral Drift Across Model Updates in Production

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Academic Research
Academic Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.22132007  33stabilfr·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]1,144✗Minimum 2,000 words for a full research article. Current: 1,144
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.22132007
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]25%✗≥60% of references from 2025–2026. Current: 25%
[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 (31 × 60%) + Required (2/5 × 30%) + Optional (1/4 × 10%)

System design for continuous behavioral regression testing of AI systems, covering snapshot testing, semantic similarity detection, and alerting for specification-violating behavioral changes. This article explores the design and implementation of a system for continuous behavioral regression testing of AI systems. We cover snapshot testing, semantic similarity detection, and alerting for speci...

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Academic Research by Oleh Ivchenko DOI: 10.5281/zenodo.22132007 33stabilfr·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]1,144✗Minimum 2,000 words for a full research article. Current: 1,144
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.22132007
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]25%✗≥60% of references from 2025–2026. Current: 25%
[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 (31 × 60%) + Required (2/5 × 30%) + Optional (1/4 × 10%)
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Self-Healing Observation Orchestrators Using Reinforcement Learning for Resource Reallocation

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Technical Research
Technical Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.22123035  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]142✗Minimum 2,000 words for a full research article. Current: 142
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.22123035
[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%)

Training reinforcement agents to dynamically reconfigure monitoring resources (e.g., sampling rates, logging granularity) in response to observed latency spikes and anomaly patterns.

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Technical Research by Oleh Ivchenko DOI: 10.5281/zenodo.22123035 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]142✗Minimum 2,000 words for a full research article. Current: 142
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.22123035
[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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Open-Source AI in Government: Procurement Barriers and Adoption Patterns in Public Sector

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Open Source Research
Open Source Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.22118897  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]696✗Minimum 2,000 words for a full research article. Current: 696
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.22118897
[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]Diagrams3✓Mermaid architecture/flow diagrams. Current: 3
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (31 × 60%) + Required (2/5 × 30%) + Optional (1/4 × 10%)

Survey of open-source AI adoption in government agencies across EU and US, identifying procurement barriers, security review requirements, and emerging patterns for successful public-sector OSS AI adoption [1]. This article examines the current state of open-source AI adoption in government agencies, focusing on procurement barriers, security review requirements, and emerging patterns for succe...

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Open Source Research by Oleh Ivchenko DOI: 10.5281/zenodo.22118897 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]696✗Minimum 2,000 words for a full research article. Current: 696
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.22118897
[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]Diagrams3✓Mermaid architecture/flow diagrams. Current: 3
[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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AI Model Sharing Economy: Designing Royalty Structures for Distributed Model Usage

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Applied Research
Applied Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.22076941  69stabilfr·wdophcgmx
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[s]Reviewed Sources58%○≥80% from editorially reviewed sources
[t]Trusted89%✓≥80% from verified, high-quality sources
[a]DOI68%○≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed79%○≥80% have metadata indexed
[l]Academic89%✓≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
[r]References19 refs✓Minimum 10 references required
[w]Words [REQ]964✗Minimum 2,000 words for a full research article. Current: 964
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.22076941
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]72%✓≥60% of references from 2025–2026. Current: 72%
[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 (81 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)

The rapid proliferation of machine l[REDACTED]g models as services has created a nascent market for model sharing, yet sustainable royalty mechanisms remain under‑explored. This article investigates how distributed model usage can be quantified, attributed, and monetized through dynamic royalty structures. We pose three research questions: (RQ1) How can usage metrics be reliably captured across...

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Applied Research by Oleh Ivchenko DOI: 10.5281/zenodo.22076941 69stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources58%○≥80% from editorially reviewed sources
[t]Trusted89%✓≥80% from verified, high-quality sources
[a]DOI68%○≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed79%○≥80% have metadata indexed
[l]Academic89%✓≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
[r]References19 refs✓Minimum 10 references required
[w]Words [REQ]964✗Minimum 2,000 words for a full research article. Current: 964
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.22076941
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]72%✓≥60% of references from 2025–2026. Current: 72%
[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 (81 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)
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