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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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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[t]Trusted94%✓≥80% from verified, high-quality sources
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Score = Ref Trust (69 × 60%) + Required (2/5 × 30%) + Optional (1/4 × 10%)
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Dynamic Model Selection under Cost Constraints: A Real-Time Decision Framework for Enterprises

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Applied Research
Applied Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.22689074  42stabilfr·wdophcgmx
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Score = Ref Trust (32 × 60%) + Required (3/5 × 30%) + Optional (2/4 × 10%)

This article presents a novel framework for dynamic model selection under cost constraints in enterprise AI systems. We introduce an adaptive algorithm that continuously monitors workload characteristics and budgetary limits to select the most cost-effective model architecture in real-time. The framework operates by profiling incoming requests, estimating inference costs and accuracy trade-offs...

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Applied Research by Oleh Ivchenko DOI: 10.5281/zenodo.22689074 42stabilfr·wdophcgmx
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[t]Trusted60%○≥80% from verified, high-quality sources
[a]DOI20%○≥80% have a Digital Object Identifier
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[h]Freshness [REQ]33%✗≥60% of references from 2025–2026. Current: 33%
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Score = Ref Trust (32 × 60%) + Required (3/5 × 30%) + Optional (2/4 × 10%)
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AI-Driven Valuation Multiples: Revisiting Equity Metrics in Companies with Embedded AI Assets

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AI Economics
AI Economics by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.22681692  66stabilfr·wdophcgmx
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[h]Freshness [REQ]67%✓≥60% of references from 2025–2026. Current: 67%
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Score = Ref Trust (75 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)

Proposes new valuation frameworks that adjust traditional multiples to reflect AI‑derived competitive advantages.

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AI Economics by Oleh Ivchenko DOI: 10.5281/zenodo.22681692 66stabilfr·wdophcgmx
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[t]Trusted80%✓≥80% from verified, high-quality sources
[a]DOI70%○≥80% have a Digital Object Identifier
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[w]Words [REQ]1,371✗Minimum 2,000 words for a full research article. Current: 1,371
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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]67%✓≥60% of references from 2025–2026. Current: 67%
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Score = Ref Trust (75 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)
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Explainable Anomaly Detection through Counterfactual Traceability in Black‑Box Systems

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Technical Research
Technical Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.22653598  31stabilfr·wdophcgmx
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[t]Trusted50%○≥80% from verified, high-quality sources
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[w]Words [REQ]535✗Minimum 2,000 words for a full research article. Current: 535
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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%)

Generating counterfactual trajectories that illustrate how observed anomalies would shift under alternative feature conditions, providing intuitive explanations for model behavior.

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Technical Research by Oleh Ivchenko DOI: 10.5281/zenodo.22653598 31stabilfr·wdophcgmx
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[t]Trusted50%○≥80% from verified, high-quality sources
[a]DOI25%○≥80% have a Digital Object Identifier
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[w]Words [REQ]535✗Minimum 2,000 words for a full research article. Current: 535
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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
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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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AI-Augmented Diplomatic Forecasting: Using Predictive Analytics to Model State Intentions in Crisis Scenarios

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Geopolitical Research
Geopolitical Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.22649193  33stabilfr·wdophcgmx
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[t]Trusted50%○≥80% from verified, high-quality sources
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[w]Words [REQ]618✗Minimum 2,000 words for a full research article. Current: 618
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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
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[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (31 × 60%) + Required (2/5 × 30%) + Optional (1/4 × 10%)

The topic of $DESCRIPTION has gained significant attention in recent years. This article aims to provide a comprehensive analysis of $DESCRIPTION, focusing on its implications for the AI industry. We begin by outlining the background and motivation for this study.

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Geopolitical Research by Oleh Ivchenko DOI: 10.5281/zenodo.22649193 33stabilfr·wdophcgmx
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[t]Trusted50%○≥80% from verified, high-quality sources
[a]DOI25%○≥80% have a Digital Object Identifier
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[w]Words [REQ]618✗Minimum 2,000 words for a full research article. Current: 618
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.22649193
[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 (31 × 60%) + Required (2/5 × 30%) + Optional (1/4 × 10%)
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Peer Review Simulation Using Generative Models: Assessing Validity of Automated Quality Ratings

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Quality Research
Quality Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.22546803  56stabilfr·wdophcgmx
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[t]Trusted100%✓≥80% from verified, high-quality sources
[a]DOI67%○≥80% have a Digital Object Identifier
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[i]Indexed0%○≥80% have metadata indexed
[l]Academic100%✓≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
[r]References3 refs○Minimum 10 references required
[w]Words [REQ]2,004✓Minimum 2,000 words for a full research article. Current: 2,004
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.22546803
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]50%✗≥60% of references from 2025–2026. Current: 50%
[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 (64 × 60%) + Required (3/5 × 30%) + Optional (0/4 × 10%)

This article presents a simulation study employing generative AI models to act as synthetic reviewers for evaluating the quality of AI research articles. We assess the validity of automated quality ratings by comparing synthetic reviewer scores with expert human evaluations across a corpus of peer-reviewed AI literature. Our findings indicate that while generative models can approximate certain...

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Quality Research by Oleh Ivchenko DOI: 10.5281/zenodo.22546803 56stabilfr·wdophcgmx
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[t]Trusted100%✓≥80% from verified, high-quality sources
[a]DOI67%○≥80% have a Digital Object Identifier
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[l]Academic100%✓≥80% from journals/conferences/preprints
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[w]Words [REQ]2,004✓Minimum 2,000 words for a full research article. Current: 2,004
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.22546803
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]50%✗≥60% of references from 2025–2026. Current: 50%
[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 (64 × 60%) + Required (3/5 × 30%) + Optional (0/4 × 10%)
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ROI Measurement Ecosystem: Designing Feedback Mechanisms for Long-Term AI Investment Returns

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Gap Research
Gap Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.22512339  56stabilfr·wdophcgmx
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[t]Trusted100%✓≥80% from verified, high-quality sources
[a]DOI50%○≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
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[l]Academic100%✓≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
[r]References2 refs○Minimum 10 references required
[w]Words [REQ]285✗Minimum 2,000 words for a full research article. Current: 285
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.22512339
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]100%✓≥60% of references from 2025–2026. Current: 100%
[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 (59 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)

Proposes a framework for tracking delayed returns from AI projects, incorporating both financial and operational indicators.

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Gap Research by Oleh Ivchenko DOI: 10.5281/zenodo.22512339 56stabilfr·wdophcgmx
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[t]Trusted100%✓≥80% from verified, high-quality sources
[a]DOI50%○≥80% have a Digital Object Identifier
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[l]Academic100%✓≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
[r]References2 refs○Minimum 10 references required
[w]Words [REQ]285✗Minimum 2,000 words for a full research article. Current: 285
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.22512339
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]100%✓≥60% of references from 2025–2026. Current: 100%
[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 (59 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)
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Contributor Economics in Open-Source AI Projects: Who Pays for Open Weights and Why

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Open Source Research
Open Source Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.22432198  39stabilfr·wdophcgmx
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[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted60%○≥80% from verified, high-quality sources
[a]DOI40%○≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed0%○≥80% have metadata indexed
[l]Academic60%○≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
[r]References5 refs○Minimum 10 references required
[w]Words [REQ]1,953✗Minimum 2,000 words for a full research article. Current: 1,953
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.22432198
[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]Diagrams4✓Mermaid architecture/flow diagrams. Current: 4
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (40 × 60%) + Required (2/5 × 30%) + Optional (1/4 × 10%)

This article investigates the economic models funding open-source AI development, addressing the critical question of who pays for open weights and why. We analyze contribution patterns across corporate, academic, and independent developers, synthesizing evidence from recent economic models, funding mechanisms, and empirical studies of contributor behavior. Our findings reveal a hybrid funding ...

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Open Source Research by Oleh Ivchenko DOI: 10.5281/zenodo.22432198 39stabilfr·wdophcgmx
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[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted60%○≥80% from verified, high-quality sources
[a]DOI40%○≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed0%○≥80% have metadata indexed
[l]Academic60%○≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
[r]References5 refs○Minimum 10 references required
[w]Words [REQ]1,953✗Minimum 2,000 words for a full research article. Current: 1,953
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.22432198
[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]Diagrams4✓Mermaid architecture/flow diagrams. Current: 4
[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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Resilience Forecasting for AI-Enabled Critical Networks: Predicting Cascading Failures Under Adversarial Stress

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Geopolitical Research
Geopolitical Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.22344805  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]122✗Minimum 2,000 words for a full research article. Current: 122
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.22344805
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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%)

Introduction Builds stochastic models to anticipate failure propagation in supply chains and communication grids when AI components are compromised.

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Geopolitical Research by Oleh Ivchenko DOI: 10.5281/zenodo.22344805 31stabilfr·wdophcgmx
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[t]Trusted50%○≥80% from verified, high-quality sources
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[r]References4 refs○Minimum 10 references required
[w]Words [REQ]122✗Minimum 2,000 words for a full research article. Current: 122
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.22344805
[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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[g]Code—○Source code available on GitHub
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Score = Ref Trust (31 × 60%) + Required (2/5 × 30%) + Optional (0/4 × 10%)
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AI Editorial Bias Detection: Mapping Demographic Skew in Automated Peer Review Scores

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Quality Research
Quality Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.22314054  31stabilfr·wdophcgmx
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[f]Free Access100%✓≥80% are freely accessible
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[w]Words [REQ]174✗Minimum 2,000 words for a full research article. Current: 174
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[h]Freshness [REQ]33%✗≥60% of references from 2025–2026. Current: 33%
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Score = Ref Trust (31 × 60%) + Required (2/5 × 30%) + Optional (0/4 × 10%)

This article addresses Identifies systematic biases in AI‑driven review processes and suggests corrective calibration techniques..

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Quality Research by Oleh Ivchenko DOI: 10.5281/zenodo.22314054 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
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[f]Free Access100%✓≥80% are freely accessible
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[w]Words [REQ]174✗Minimum 2,000 words for a full research article. Current: 174
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Score = Ref Trust (31 × 60%) + Required (2/5 × 30%) + Optional (0/4 × 10%)
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Benchmarking AI Operational Costs: Towards an Industry Standard for Cost Attribution

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Applied Research
Applied Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.22309605  53stabilfr·wdophcgmx
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[a]DOI50%○≥80% have a Digital Object Identifier
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[l]Academic100%✓≥80% from journals/conferences/preprints
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[g]Code—○Source code available on GitHub
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[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (59 × 60%) + Required (3/5 × 30%) + Optional (0/4 × 10%)

}/Benchmarking AI Operational Costs: Towards an Industry Standard for Cost Attribution}}/Proposes a standardized set of metrics and reporting practices for transparent AI cost accounting.}}/}}/}}/}}/[w] Words ; [c] Data Charts ; [g] Code ; [r] only 0 refs (need 10); badge pct 0% (need 70%)}}/}

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Applied Research by Oleh Ivchenko DOI: 10.5281/zenodo.22309605 53stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted100%✓≥80% from verified, high-quality sources
[a]DOI50%○≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
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[l]Academic100%✓≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
[r]References2 refs○Minimum 10 references required
[w]Words [REQ]94✗Minimum 2,000 words for a full research article. Current: 94
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[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]100%✓≥60% of references from 2025–2026. Current: 100%
[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 (59 × 60%) + Required (3/5 × 30%) + Optional (0/4 × 10%)
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