Category: AI Economics

AI Economics: Risk, Cost, and ROI Research by Oleh Ivchenko

The LLM Commoditization Playbook: How Enterprises Exploit Price Competition Between Providers

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AI Economics by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.21729348  60stabilfr·wdophcgmx
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[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (65 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)

Enterprises increasingly deploy large language models (LLMs) as core components of their digital stack, yet the cost structure of provider access remains fragmented and opaque. This article investigates how organizations can systematically exploit price competition among LLM providers to achieve measurable reductions in inference spend. We frame the problem as a procurement challenge, map curre...

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AI Economics by Oleh Ivchenko DOI: 10.5281/zenodo.21729348 60stabilfr·wdophcgmx
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[w]Words [REQ]1,270✗Minimum 2,000 words for a full research article. Current: 1,270
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.21729348
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]76%✓≥60% of references from 2025–2026. Current: 76%
[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 (65 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)
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The Capability Adoption Stack: A Framework for Diagnosing Enterprise AI Readiness

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AI Economics by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.21715745  49stabilfr·wdophcgmx
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[l]Academic91%✓≥80% from journals/conferences/preprints
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[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]87%✓≥60% of references from 2025–2026. Current: 87%
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[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 (48 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)

Enterprise AI adoption remains fragmented, with organizations struggling to diagnose their readiness across technical, human, and governance dimensions[1]. This article introduces the Capability Adoption Stack (CAS), a layered diagnostic model that maps data infrastructure, talent, governance, and integration maturity into a coherent framework[2]. By operationalizing these dimensions into measu...

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AI Economics by Oleh Ivchenko DOI: 10.5281/zenodo.21715745 49stabilfr·wdophcgmx
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[t]Trusted94%✓≥80% from verified, high-quality sources
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[l]Academic91%✓≥80% from journals/conferences/preprints
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[w]Words [REQ]1,441✗Minimum 2,000 words for a full research article. Current: 1,441
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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]87%✓≥60% of references from 2025–2026. Current: 87%
[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 (48 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)
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AI Subscription Economics: How Flat-Rate Pricing Masks True Enterprise AI Costs

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AI Economics by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.21644874  65stabilfr·wdophcgmx
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[t]Trusted100%✓≥80% from verified, high-quality sources
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[l]Academic100%✓≥80% from journals/conferences/preprints
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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]6%✗≥60% of references from 2025–2026. Current: 6%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams6✓Mermaid architecture/flow diagrams. Current: 6
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (74 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)

Flat‑rate pricing models for enterprise AI services promise predictable cost structures, yet they often obscure significant hidden expenditures that can distort true total cost of ownership. This article uncovers three categories of concealed costs—compute overages, integration and support burdens, and switching costs—that emerge when organizations adopt seemingly straightforward subscription p...

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AI Economics by Oleh Ivchenko DOI: 10.5281/zenodo.21644874 65stabilfr·wdophcgmx
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[t]Trusted100%✓≥80% from verified, high-quality sources
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[l]Academic100%✓≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
[r]References18 refs✓Minimum 10 references required
[w]Words [REQ]2,017✓Minimum 2,000 words for a full research article. Current: 2,017
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.21644874
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]6%✗≥60% of references from 2025–2026. Current: 6%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams6✓Mermaid architecture/flow diagrams. Current: 6
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (74 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)
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Foundation Model Commoditization: How API Parity Is Reshaping the AI Stack in 2025

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AI Economics by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.21550109  61stabilfr·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]84%✓≥60% of references from 2025–2026. Current: 84%
[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 (67 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)

In this article we analyze the commoditization of foundation model APIs, showing how parity in capability metrics is reshaping competitive dynamics across the AI stack in 2025. Using a mixed-methods approach combining benchmark analysis, cost modeling, and market trend evaluation, we identify three dominant research questions: (RQ1) How have benchmark scores converged across leading frontier mo...

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AI Economics by Oleh Ivchenko DOI: 10.5281/zenodo.21550109 61stabilfr·wdophcgmx
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[a]DOI85%✓≥80% have a Digital Object Identifier
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[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.21550109
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]84%✓≥60% of references from 2025–2026. Current: 84%
[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 (67 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)
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Shadow Banking Detection with Graph Neural Networks: Mapping Unofficial Lending Networks

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AI Economics by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.21535837  61stabilfr·wdophcgmx
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[t]Trusted92%✓≥80% from verified, high-quality sources
[a]DOI85%✓≥80% have a Digital Object Identifier
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[i]Indexed0%○≥80% have metadata indexed
[l]Academic92%✓≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
[r]References26 refs✓Minimum 10 references required
[w]Words [REQ]1,948✗Minimum 2,000 words for a full research article. Current: 1,948
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.21535837
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]84%✓≥60% of references from 2025–2026. Current: 84%
[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 (68 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)

Shadow banking has emerged as a critical stress point in the global financial system, enabling credit expansion outside regulated intermediation. This article develops a graph‑based anomaly detection framework that maps unofficial lending networks using transaction‑level data from Eastern European regulatory pilots. By treating financial entities as nodes and payment flows as weighted edges, we...

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AI Economics by Oleh Ivchenko DOI: 10.5281/zenodo.21535837 61stabilfr·wdophcgmx
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[t]Trusted92%✓≥80% from verified, high-quality sources
[a]DOI85%✓≥80% have a Digital Object Identifier
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[l]Academic92%✓≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
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[w]Words [REQ]1,948✗Minimum 2,000 words for a full research article. Current: 1,948
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.21535837
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]84%✓≥60% of references from 2025–2026. Current: 84%
[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 (68 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)
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Opportunity Cost of AI Waiting: Economic Modeling of Delayed Enterprise AI Adoption

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AI Economics by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.21505176  55stabilfr·wdophcgmx
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[t]Trusted92%✓≥80% from verified, high-quality sources
[a]DOI83%✓≥80% have a Digital Object Identifier
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[i]Indexed0%○≥80% have metadata indexed
[l]Academic92%✓≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
[r]References12 refs✓Minimum 10 references required
[w]Words [REQ]1,525✗Minimum 2,000 words for a full research article. Current: 1,525
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.21505176
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]58%✗≥60% of references from 2025–2026. Current: 58%
[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 (67 × 60%) + Required (2/5 × 30%) + Optional (1/4 × 10%)

Enterprises that delay AI adoption face a measurable competitive disadvantage that can be quantified in economic terms. This article answers three research questions: (RQ1) What is the average competitive disadvantage cost for enterprises that delay AI adoption by 12–24 months relative to early adopters? (RQ2) Which industry sectors exhibit the highest cost differentials? (RQ3) What are the mac...

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AI Economics by Oleh Ivchenko DOI: 10.5281/zenodo.21505176 55stabilfr·wdophcgmx
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[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted92%✓≥80% from verified, high-quality sources
[a]DOI83%✓≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed0%○≥80% have metadata indexed
[l]Academic92%✓≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
[r]References12 refs✓Minimum 10 references required
[w]Words [REQ]1,525✗Minimum 2,000 words for a full research article. Current: 1,525
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.21505176
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]58%✗≥60% of references from 2025–2026. Current: 58%
[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 (67 × 60%) + Required (2/5 × 30%) + Optional (1/4 × 10%)
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AI-Driven Market Concentration: Measuring Oligopoly Risk in Foundation Model Economics

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AI Economics by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.21434928  64stabilfr·wdophcgmx
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[t]Trusted93%✓≥80% from verified, high-quality sources
[a]DOI86%✓≥80% have a Digital Object Identifier
[b]CrossRef7%○≥80% indexed in CrossRef
[i]Indexed14%○≥80% have metadata indexed
[l]Academic93%✓≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
[r]References14 refs✓Minimum 10 references required
[w]Words [REQ]650✗Minimum 2,000 words for a full research article. Current: 650
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.21434928
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]62%✓≥60% of references from 2025–2026. Current: 62%
[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 (72 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)

Foundation models are increasingly central to digital economies, yet their market structure remains sparsely quantified. This article asks how economic dynamics of winner‑take‑all markets manifest in the foundation‑model sector, what barriers to entry shape competitive equilibrium, and how switching costs influence firm behavior. We address these questions through a three‑pronged empirical stra...

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AI Economics by Oleh Ivchenko DOI: 10.5281/zenodo.21434928 64stabilfr·wdophcgmx
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[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted93%✓≥80% from verified, high-quality sources
[a]DOI86%✓≥80% have a Digital Object Identifier
[b]CrossRef7%○≥80% indexed in CrossRef
[i]Indexed14%○≥80% have metadata indexed
[l]Academic93%✓≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
[r]References14 refs✓Minimum 10 references required
[w]Words [REQ]650✗Minimum 2,000 words for a full research article. Current: 650
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.21434928
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]62%✓≥60% of references from 2025–2026. Current: 62%
[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 (72 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)
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The Inference Cost Collapse: Economic Implications of 10x Annual Price Reductions for LLM APIs

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AI Economics by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.21363672  36stabilfr·wdophcgmx
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[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]67✗Minimum 2,000 words for a full research article. Current: 67
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.21363672
[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 (40 × 60%) + Required (2/5 × 30%) + Optional (0/4 × 10%)

Academic Citation: Ivchenko, Oleh, Ivchenko, Iryna (2026). The Inference Cost Collapse: Economic Implications of 10x Annual Price Reductions for LLM APIs. Research article: The Inference Cost Collapse: Economic Implications of 10x Annual Price Reductions for LLM APIs. Odessa National Polytechnic University, Department of Economic Cybernetics. DOI: 10.5281/zenodo.21363672  ·  View on Z...

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AI Economics by Oleh Ivchenko DOI: 10.5281/zenodo.21363672 36stabilfr·wdophcgmx
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[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]67✗Minimum 2,000 words for a full research article. Current: 67
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.21363672
[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 (40 × 60%) + Required (2/5 × 30%) + Optional (0/4 × 10%)
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OSS AI License Compliance: Legal Risks and Enterprise Obligations Under Custom AI Licenses

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AI Economics by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.21311225  66stabilfr·wdophcgmx
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This article investigates the legal ramifications of non‑OSI‑approved AI licenses promulgated by major technology firms—including Meta’s Llama, Falcon, and Anthropic’s Claude—on enterprise adoption of open‑weight models. We frame the problem through three research questions: (RQ1) What contractual and regulatory obligations arise when deploying models distributed under custom licenses? (RQ2) Ho...

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AI Economics by Oleh Ivchenko DOI: 10.5281/zenodo.21311225 66stabilfr·wdophcgmx
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[s]Reviewed Sources0%○≥80% from editorially reviewed sources
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[a]DOI98%✓≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed0%○≥80% have metadata indexed
[l]Academic100%✓≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
[r]References46 refs✓Minimum 10 references required
[w]Words [REQ]1,296✗Minimum 2,000 words for a full research article. Current: 1,296
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.21311225
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]96%✓≥60% of references from 2025–2026. Current: 96%
[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 (75 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)
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AI Wage Premium Evidence: Measuring Productivity Uplift and Salary Effects in Knowledge Work

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AI Economics by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.21270839  64stabilfr·wdophcgmx
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[s]Reviewed Sources0%○≥80% from editorially reviewed sources
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[a]DOI89%✓≥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]References18 refs✓Minimum 10 references required
[w]Words [REQ]926✗Minimum 2,000 words for a full research article. Current: 926
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.21270839
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The rapid adoption of artificial intelligence (AI) technologies in knowledge-intensive industries has sparked debate about its impact on compensation structures and wage inequality. This article investigates whether AI tools increase or decrease salary differentials across skill levels and firm sizes, focusing on empirical evidence from firms with high AI penetration. We pose three research que...

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AI Economics by Oleh Ivchenko DOI: 10.5281/zenodo.21270839 64stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted100%✓≥80% from verified, high-quality sources
[a]DOI89%✓≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed0%○≥80% have metadata indexed
[l]Academic100%✓≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
[r]References18 refs✓Minimum 10 references required
[w]Words [REQ]926✗Minimum 2,000 words for a full research article. Current: 926
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.21270839
[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]Diagrams2✓Mermaid architecture/flow diagrams. Current: 2
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (72 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)
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