The Open Humanoid: Why We Are Building a Robot From First Principles

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Engineering Research
Engineering Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18946968  66stabilfr·wuedophcgmx
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In February 2026, Boston Dynamics announced that its electric Atlas humanoid had entered production and begun autonomous operation in commercial facilities. The robot stands approximately 1.5 meters tall, weighs 89 kilograms, features 28 degrees of freedom, and can perform dynamic movements that were science fiction a decade ago. Tesla claims its Optimus robot will achieve commercial deployment...

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Engineering Research by Oleh Ivchenko DOI: 10.5281/zenodo.18946968 66stabilfr·wuedophcgmx
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[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted71%○≥80% from verified, high-quality sources
[a]DOI29%○≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed71%○≥80% have metadata indexed
[l]Academic57%○≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
[r]References7 refs○Minimum 10 references required
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[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 (55 × 60%) + Required (4/4 × 30%) + Optional (2/7 × 10%)
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Why Companies Don’t Want You to Know the Real Cost of AI

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Applied Research
Applied Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18944159  53stabilfr·wuedophcgmx
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[a]DOI17%○≥80% have a Digital Object Identifier
[b]CrossRef6%○≥80% indexed in CrossRef
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Score = Ref Trust (33 × 60%) + Required (4/4 × 30%) + Optional (2/7 × 10%)

The current landscape of artificial intelligence pricing operates on a fundamental deception: what consumers pay bears almost no relationship to what the technology actually costs. This paper explores the economic mechanics behind platform subsidisation, the strategic motivations for concealing true costs, and the implications for enterprises building AI-powered products. Drawing on platform ec...

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Applied Research by Oleh Ivchenko DOI: 10.5281/zenodo.18944159 53stabilfr·wuedophcgmx
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[s]Reviewed Sources6%○≥80% from editorially reviewed sources
[t]Trusted39%○≥80% from verified, high-quality sources
[a]DOI17%○≥80% have a Digital Object Identifier
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[r]References18 refs✓Minimum 10 references required
[w]Words [REQ]2,868✓Minimum 1,000 words of article body. Current: 2,868
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[p]Peer Reviewed—○Peer reviewed by an assigned reviewer
[h]Freshness28%○≥60% of references from 2025–2026. Current: 28%
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[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (33 × 60%) + Required (4/4 × 30%) + Optional (2/7 × 10%)
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The Subsidised Intelligence Illusion: What AI Really Costs When the Platform Isn’t Paying

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Applied Research
Applied Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18943388  50stabilfr·wuedophcgmx
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[r]References16 refs✓Minimum 10 references required
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Enterprise AI adoption has accelerated dramatically, yet fundamental cost misperceptions persist. This paper demonstrates that consumer subscription plans for frontier AI models (Claude Max at $100/month, ChatGPT Plus at $20/month) represent heavily platform-subsidised pricing that bears no relation to actual inference economics. Through detailed token consumption analysis and API pricing calcu...

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Applied Research by Oleh Ivchenko DOI: 10.5281/zenodo.18943388 50stabilfr·wuedophcgmx
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[s]Reviewed Sources6%○≥80% from editorially reviewed sources
[t]Trusted25%○≥80% from verified, high-quality sources
[a]DOI19%○≥80% have a Digital Object Identifier
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[l]Academic25%○≥80% from journals/conferences/preprints
[f]Free Access44%○≥80% are freely accessible
[r]References16 refs✓Minimum 10 references required
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[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (29 × 60%) + Required (4/4 × 30%) + Optional (2/7 × 10%)
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Agent Economy Investment Surge: VC Bets on Agentic Infrastructure

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AI Economics
AI Economics by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18943141  47stabilfr·wuedophcgmx
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[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (21 × 60%) + Required (4/4 × 30%) + Optional (3/7 × 10%)

February 2026 produced the largest monthly venture capital figure ever recorded: $189 billion, of which AI startups captured $171 billion — 90% of the total. Three companies (OpenAI, Anthropic, Waymo) accounted for 83% of that sum alone. But beneath the headline megadeals, a quieter structural shift is underway: seed and Series A funding is flowing specifically into agentic infrastructure — the...

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AI Economics by Oleh Ivchenko DOI: 10.5281/zenodo.18943141 47stabilfr·wuedophcgmx
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[a]DOI17%○≥80% have a Digital Object Identifier
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[p]Peer Reviewed—○Peer reviewed by an assigned reviewer
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Score = Ref Trust (21 × 60%) + Required (4/4 × 30%) + Optional (3/7 × 10%)
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Agent Auditor — Part 3: Career Landscape & Market Forecast

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Journal Commentary
Journal Commentary by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18930666  50stabilfr·wuedophcgmx
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[l]Academic21%○≥80% from journals/conferences/preprints
[f]Free Access50%○≥80% are freely accessible
[r]References14 refs✓Minimum 10 references required
[w]Words [REQ]2,412✓Minimum 1,000 words of article body. Current: 2,412
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[o]ORCID✓✓Author ORCID verified for academic identity
[p]Peer Reviewed—○Peer reviewed by an assigned reviewer
[h]Freshness31%○≥60% of references from 2025–2026. Current: 31%
[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 (28 × 60%) + Required (4/4 × 30%) + Optional (2/7 × 10%)

Parts 1 and 2 of this series established the structural case for the Agent Auditor as a distinct professional role and mapped the competency model required to fill it. This final instalment examines the market reality: where the demand is forming, what it pays, which sectors are driving adoption, and how the regulatory environment — in particular the EU AI Act — is accelerating the transition f...

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Journal Commentary by Oleh Ivchenko DOI: 10.5281/zenodo.18930666 50stabilfr·wuedophcgmx
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[t]Trusted36%○≥80% from verified, high-quality sources
[a]DOI14%○≥80% have a Digital Object Identifier
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[o]ORCID✓✓Author ORCID verified for academic identity
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[h]Freshness31%○≥60% of references from 2025–2026. Current: 31%
[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 (28 × 60%) + Required (4/4 × 30%) + Optional (2/7 × 10%)
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From a Destroyed City to a Research Hub: The Story Behind Stabilarity

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DOI: 10.5281/zenodo.18930087  51stabilfr·wuedophcgmx
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[t]Trusted38%○≥80% from verified, high-quality sources
[a]DOI19%○≥80% have a Digital Object Identifier
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[l]Academic19%○≥80% from journals/conferences/preprints
[f]Free Access63%○≥80% are freely accessible
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Score = Ref Trust (30 × 60%) + Required (4/4 × 30%) + Optional (2/7 × 10%)

The story starts in a classroom, as most research stories do — though this particular classroom was unofficial. Around 2019, Oleh Ivchenko began running supplementary IT courses at Odessa National Polytechnic University. Not because the institution asked him to, but because the gap between what students were being taught and what the industry actually needed had become too large to ignore. He r...

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DOI: 10.5281/zenodo.18930087 51stabilfr·wuedophcgmx
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[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted38%○≥80% from verified, high-quality sources
[a]DOI19%○≥80% have a Digital Object Identifier
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[p]Peer Reviewed—○Peer reviewed by an assigned reviewer
[h]Freshness23%○≥60% of references from 2025–2026. Current: 23%
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[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (30 × 60%) + Required (4/4 × 30%) + Optional (2/7 × 10%)
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Tattoo-Based Emergency Patient Identification: From Internal Research to Public Deployment

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Medical Research
Medical Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18929669  70stabilfr·wuedophcgmx
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[s]Reviewed Sources25%○≥80% from editorially reviewed sources
[t]Trusted83%✓≥80% from verified, high-quality sources
[a]DOI58%○≥80% have a Digital Object Identifier
[b]CrossRef25%○≥80% indexed in CrossRef
[i]Indexed33%○≥80% have metadata indexed
[l]Academic58%○≥80% from journals/conferences/preprints
[f]Free Access75%○≥80% are freely accessible
[r]References12 refs✓Minimum 10 references required
[w]Words [REQ]1,983✓Minimum 1,000 words of article body. Current: 1,983
[u]Original [REQ]100%✓≥85% unique sentences (no padding or repeated blocks) and no leaked drafting notes. Current: 100% unique
[e]Evidence [REQ]8✓Cites at least 1 external source(s): links, DOIs or a reference list. Current: 8
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18929669
[o]ORCID✓✓Author ORCID verified for academic identity
[p]Peer Reviewed—○Peer reviewed by an assigned reviewer
[h]Freshness10%○≥60% of references from 2025–2026. Current: 10%
[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 (62 × 60%) + Required (4/4 × 30%) + Optional (2/7 × 10%)

We describe the public release of a tattoo-based emergency patient identification framework whose conceptual roots trace to OTG-bot — a UNDP-grant-winning civic technology project developed in 2021 for Ukrainian territorial communities. That project received a $10,000 USD grant from the United Nations Development Programme at the Hack Locals 2.0 hackathon and included an automated missing-perso...

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Medical Research by Oleh Ivchenko DOI: 10.5281/zenodo.18929669 70stabilfr·wuedophcgmx
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[s]Reviewed Sources25%○≥80% from editorially reviewed sources
[t]Trusted83%✓≥80% from verified, high-quality sources
[a]DOI58%○≥80% have a Digital Object Identifier
[b]CrossRef25%○≥80% indexed in CrossRef
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[l]Academic58%○≥80% from journals/conferences/preprints
[f]Free Access75%○≥80% are freely accessible
[r]References12 refs✓Minimum 10 references required
[w]Words [REQ]1,983✓Minimum 1,000 words of article body. Current: 1,983
[u]Original [REQ]100%✓≥85% unique sentences (no padding or repeated blocks) and no leaked drafting notes. Current: 100% unique
[e]Evidence [REQ]8✓Cites at least 1 external source(s): links, DOIs or a reference list. Current: 8
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18929669
[o]ORCID✓✓Author ORCID verified for academic identity
[p]Peer Reviewed—○Peer reviewed by an assigned reviewer
[h]Freshness10%○≥60% of references from 2025–2026. Current: 10%
[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 (62 × 60%) + Required (4/4 × 30%) + Optional (2/7 × 10%)
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Longitudinal Report Generation with LLM-Based Agents: Architecture, Consistency Mechanisms, and Empirical Evidence

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DOI: 10.5281/zenodo.18928461  76stabilfr·wuedophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources4%○≥80% from editorially reviewed sources
[t]Trusted92%✓≥80% from verified, high-quality sources
[a]DOI92%✓≥80% have a Digital Object Identifier
[b]CrossRef16%○≥80% indexed in CrossRef
[i]Indexed8%○≥80% have metadata indexed
[l]Academic92%✓≥80% from journals/conferences/preprints
[f]Free Access92%✓≥80% are freely accessible
[r]References25 refs✓Minimum 10 references required
[w]Words [REQ]3,189✓Minimum 1,000 words of article body. Current: 3,189
[u]Original [REQ]100%✓≥85% unique sentences (no padding or repeated blocks) and no leaked drafting notes. Current: 100% unique
[e]Evidence [REQ]22✓Cites at least 1 external source(s): links, DOIs or a reference list. Current: 22
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18928461
[o]ORCID✓✓Author ORCID verified for academic identity
[p]Peer Reviewed—○Peer reviewed by an assigned reviewer
[h]Freshness38%○≥60% of references from 2025–2026. Current: 38%
[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 (4/4 × 30%) + Optional (2/7 × 10%)

Large language model (LLM) based agents are increasingly deployed as autonomous report-generation systems — producing research summaries, analytical outputs, and monitoring digests across extended time horizons without continuous human supervision. This paper examines the fundamental challenges of longitudinal consistency in such systems: context window exhaustion, semantic drift, hallucination...

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DOI: 10.5281/zenodo.18928461 76stabilfr·wuedophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources4%○≥80% from editorially reviewed sources
[t]Trusted92%✓≥80% from verified, high-quality sources
[a]DOI92%✓≥80% have a Digital Object Identifier
[b]CrossRef16%○≥80% indexed in CrossRef
[i]Indexed8%○≥80% have metadata indexed
[l]Academic92%✓≥80% from journals/conferences/preprints
[f]Free Access92%✓≥80% are freely accessible
[r]References25 refs✓Minimum 10 references required
[w]Words [REQ]3,189✓Minimum 1,000 words of article body. Current: 3,189
[u]Original [REQ]100%✓≥85% unique sentences (no padding or repeated blocks) and no leaked drafting notes. Current: 100% unique
[e]Evidence [REQ]22✓Cites at least 1 external source(s): links, DOIs or a reference list. Current: 22
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18928461
[o]ORCID✓✓Author ORCID verified for academic identity
[p]Peer Reviewed—○Peer reviewed by an assigned reviewer
[h]Freshness38%○≥60% of references from 2025–2026. Current: 38%
[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 (4/4 × 30%) + Optional (2/7 × 10%)
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Beyond the Benchmark: What AI Looks Like When It Actually Works

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DOI: 10.5281/zenodo.18926904  74stabilfr·wuedophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources33%○≥80% from editorially reviewed sources
[t]Trusted78%○≥80% from verified, high-quality sources
[a]DOI61%○≥80% have a Digital Object Identifier
[b]CrossRef17%○≥80% indexed in CrossRef
[i]Indexed61%○≥80% have metadata indexed
[l]Academic61%○≥80% from journals/conferences/preprints
[f]Free Access56%○≥80% are freely accessible
[r]References18 refs✓Minimum 10 references required
[w]Words [REQ]2,294✓Minimum 1,000 words of article body. Current: 2,294
[u]Original [REQ]99%✓≥85% unique sentences (no padding or repeated blocks) and no leaked drafting notes. Current: 99% unique
[e]Evidence [REQ]12✓Cites at least 1 external source(s): links, DOIs or a reference list. Current: 12
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18926904
[o]ORCID✓✓Author ORCID verified for academic identity
[p]Peer Reviewed—○Peer reviewed by an assigned reviewer
[h]Freshness31%○≥60% of references from 2025–2026. Current: 31%
[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 (68 × 60%) + Required (4/4 × 30%) + Optional (2/7 × 10%)

The most consequential question in applied artificial intelligence is not whether a model achieves state-of-the-art on a leaderboard. It is whether the model does something useful when connected to reality — to messy data, constrained infrastructure, and users who need answers rather than probabilities. This article examines what AI actually looks like when it crosses that boundary. Drawing on ...

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DOI: 10.5281/zenodo.18926904 74stabilfr·wuedophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources33%○≥80% from editorially reviewed sources
[t]Trusted78%○≥80% from verified, high-quality sources
[a]DOI61%○≥80% have a Digital Object Identifier
[b]CrossRef17%○≥80% indexed in CrossRef
[i]Indexed61%○≥80% have metadata indexed
[l]Academic61%○≥80% from journals/conferences/preprints
[f]Free Access56%○≥80% are freely accessible
[r]References18 refs✓Minimum 10 references required
[w]Words [REQ]2,294✓Minimum 1,000 words of article body. Current: 2,294
[u]Original [REQ]99%✓≥85% unique sentences (no padding or repeated blocks) and no leaked drafting notes. Current: 99% unique
[e]Evidence [REQ]12✓Cites at least 1 external source(s): links, DOIs or a reference list. Current: 12
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18926904
[o]ORCID✓✓Author ORCID verified for academic identity
[p]Peer Reviewed—○Peer reviewed by an assigned reviewer
[h]Freshness31%○≥60% of references from 2025–2026. Current: 31%
[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 (68 × 60%) + Required (4/4 × 30%) + Optional (2/7 × 10%)
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Stabilarity Research Platform Is Now Open — Free API Access for All Researchers

Posted on by Admin
DOI: 10.5281/zenodo.18928330  74stabilfr·wuedcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources35%○≥80% from editorially reviewed sources
[t]Trusted94%✓≥80% from verified, high-quality sources
[a]DOI41%○≥80% have a Digital Object Identifier
[b]CrossRef24%○≥80% indexed in CrossRef
[i]Indexed82%✓≥80% have metadata indexed
[l]Academic41%○≥80% from journals/conferences/preprints
[f]Free Access65%○≥80% are freely accessible
[r]References17 refs✓Minimum 10 references required
[w]Words [REQ]1,790✓Minimum 1,000 words of article body. Current: 1,790
[u]Original [REQ]100%✓≥85% unique sentences (no padding or repeated blocks) and no leaked drafting notes. Current: 100% unique
[e]Evidence [REQ]7✓Cites at least 1 external source(s): links, DOIs or a reference list. Current: 7
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18928330
[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 (69 × 60%) + Required (4/4 × 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 74stabilfr·wuedcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources35%○≥80% from editorially reviewed sources
[t]Trusted94%✓≥80% from verified, high-quality sources
[a]DOI41%○≥80% have a Digital Object Identifier
[b]CrossRef24%○≥80% indexed in CrossRef
[i]Indexed82%✓≥80% have metadata indexed
[l]Academic41%○≥80% from journals/conferences/preprints
[f]Free Access65%○≥80% are freely accessible
[r]References17 refs✓Minimum 10 references required
[w]Words [REQ]1,790✓Minimum 1,000 words of article body. Current: 1,790
[u]Original [REQ]100%✓≥85% unique sentences (no padding or repeated blocks) and no leaked drafting notes. Current: 100% unique
[e]Evidence [REQ]7✓Cites at least 1 external source(s): links, DOIs or a reference list. Current: 7
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18928330
[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 (69 × 60%) + Required (4/4 × 30%) + Optional (1/4 × 10%)
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