Category: Anticipatory Intelligence

Anticipatory Intelligence Gap Research by Dmytro Grybeniuk

Gap Analysis: Cross-Domain Transfer of Anticipatory Models

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Academic Research by Dmytro Grybeniuk & Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18682333  70stabilfr·wdophcgmx
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[t]Trusted100%✓≥80% from verified, high-quality sources
[a]DOI100%✓≥80% have a Digital Object Identifier
[b]CrossRef74%○≥80% indexed in CrossRef
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[l]Academic100%✓≥80% from journals/conferences/preprints
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[w]Words [REQ]5,712✓Minimum 2,000 words for a full research article. Current: 5,712
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[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (83 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)

Anticipatory intelligence systems — those designed not merely to detect current states but to model causal futures — are expensive to build. Enormously, stubbornly expensive. The data pipelines, domain expert annotation, temporal calibration, and causal graph engineering that underpin a production-grade anticipatory model in, say, pharmaceutical demand forecasting represent years of investment ...

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Academic Research by Dmytro Grybeniuk & Oleh Ivchenko DOI: 10.5281/zenodo.18682333 70stabilfr·wdophcgmx
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[s]Reviewed Sources61%○≥80% from editorially reviewed sources
[t]Trusted100%✓≥80% from verified, high-quality sources
[a]DOI100%✓≥80% have a Digital Object Identifier
[b]CrossRef74%○≥80% indexed in CrossRef
[i]Indexed6%○≥80% have metadata indexed
[l]Academic100%✓≥80% from journals/conferences/preprints
[f]Free Access26%○≥80% are freely accessible
[r]References31 refs✓Minimum 10 references required
[w]Words [REQ]5,712✓Minimum 2,000 words for a full research article. Current: 5,712
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18682333
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]3%✗≥60% of references from 2025–2026. Current: 3%
[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 (83 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)
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Gap Analysis: Real-Time Adaptation to Distribution Shift

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Academic Research by Dmytro Grybeniuk & Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18672412  75stabilfr·wdophcgmx
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[s]Reviewed Sources33%○≥80% from editorially reviewed sources
[t]Trusted100%✓≥80% from verified, high-quality sources
[a]DOI67%○≥80% have a Digital Object Identifier
[b]CrossRef33%○≥80% indexed in CrossRef
[i]Indexed67%○≥80% have metadata indexed
[l]Academic100%✓≥80% from journals/conferences/preprints
[f]Free Access67%○≥80% are freely accessible
[r]References3 refs○Minimum 10 references required
[w]Words [REQ]4,916✓Minimum 2,000 words for a full research article. Current: 4,916
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18672412
[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%
[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 (80 × 60%) + Required (4/5 × 30%) + Optional (1/4 × 10%)

Distribution shift — the statistical divergence between the data a model trained on and the data it encounters in production — is the quiet destroyer of AI reliability. Unlike model bugs or data quality failures that manifest acutely, distribution shift degrades performance gradually, silently, until the system is making decisions optimized for a world that no longer exists. For anticipatory AI...

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Academic Research by Dmytro Grybeniuk & Oleh Ivchenko DOI: 10.5281/zenodo.18672412 75stabilfr·wdophcgmx
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[s]Reviewed Sources33%○≥80% from editorially reviewed sources
[t]Trusted100%✓≥80% from verified, high-quality sources
[a]DOI67%○≥80% have a Digital Object Identifier
[b]CrossRef33%○≥80% indexed in CrossRef
[i]Indexed67%○≥80% have metadata indexed
[l]Academic100%✓≥80% from journals/conferences/preprints
[f]Free Access67%○≥80% are freely accessible
[r]References3 refs○Minimum 10 references required
[w]Words [REQ]4,916✓Minimum 2,000 words for a full research article. Current: 4,916
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18672412
[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%
[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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Gap Analysis: Explainability-Accuracy Tradeoff in High-Stakes Domains

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Academic Research by Dmytro Grybeniuk & Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18662985  69stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources40%○≥80% from editorially reviewed sources
[t]Trusted97%✓≥80% from verified, high-quality sources
[a]DOI97%✓≥80% have a Digital Object Identifier
[b]CrossRef60%○≥80% indexed in CrossRef
[i]Indexed10%○≥80% have metadata indexed
[l]Academic97%✓≥80% from journals/conferences/preprints
[f]Free Access53%○≥80% are freely accessible
[r]References30 refs✓Minimum 10 references required
[w]Words [REQ]5,272✓Minimum 2,000 words for a full research article. Current: 5,272
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18662985
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]2%✗≥60% of references from 2025–2026. Current: 2%
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[g]Code—○Source code available on GitHub
[m]Diagrams7✓Mermaid architecture/flow diagrams. Current: 7
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (80 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)

Academic Citation: Dmytro Grybeniuk & Oleh Ivchenko. (2026). Gap Analysis: Explainability-Accuracy Tradeoff in High-Stakes Domains. Anticipatory Intelligence Series. Odessa National Polytechnic University. DOI: 10.5281/zenodo.18662985 Abstract The explainability-accuracy tradeoff represents one of the most economically consequential yet technically intractable gaps in anticipatory AI syste...

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Academic Research by Dmytro Grybeniuk & Oleh Ivchenko DOI: 10.5281/zenodo.18662985 69stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources40%○≥80% from editorially reviewed sources
[t]Trusted97%✓≥80% from verified, high-quality sources
[a]DOI97%✓≥80% have a Digital Object Identifier
[b]CrossRef60%○≥80% indexed in CrossRef
[i]Indexed10%○≥80% have metadata indexed
[l]Academic97%✓≥80% from journals/conferences/preprints
[f]Free Access53%○≥80% are freely accessible
[r]References30 refs✓Minimum 10 references required
[w]Words [REQ]5,272✓Minimum 2,000 words for a full research article. Current: 5,272
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18662985
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]2%✗≥60% of references from 2025–2026. Current: 2%
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[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (80 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)
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Anticipatory Intelligence: Gap Analysis — Cold Start Problem in Predictive Modeling

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Academic Research by Dmytro Grybeniuk & Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18648784  46stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources23%○≥80% from editorially reviewed sources
[t]Trusted54%○≥80% from verified, high-quality sources
[a]DOI26%○≥80% have a Digital Object Identifier
[b]CrossRef23%○≥80% indexed in CrossRef
[i]Indexed43%○≥80% have metadata indexed
[l]Academic31%○≥80% from journals/conferences/preprints
[f]Free Access29%○≥80% are freely accessible
[r]References35 refs✓Minimum 10 references required
[w]Words [REQ]2,819✓Minimum 2,000 words for a full research article. Current: 2,819
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18648784
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]3%✗≥60% of references from 2025–2026. Current: 3%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
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[m]Diagrams5✓Mermaid architecture/flow diagrams. Current: 5
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (43 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)

In March 2020, Quibi launched with $1.75 billion in funding, 175 employees, and zero understanding of its audience. The mobile streaming platform had assembled an impressive content library—short-form episodes from A-list creators—but possessed no historical viewing data, no user behavior patterns, and no recommendation engine capable of surfacing relevant content to new subscribers. Within six...

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Academic Research by Dmytro Grybeniuk & Oleh Ivchenko DOI: 10.5281/zenodo.18648784 46stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources23%○≥80% from editorially reviewed sources
[t]Trusted54%○≥80% from verified, high-quality sources
[a]DOI26%○≥80% have a Digital Object Identifier
[b]CrossRef23%○≥80% indexed in CrossRef
[i]Indexed43%○≥80% have metadata indexed
[l]Academic31%○≥80% from journals/conferences/preprints
[f]Free Access29%○≥80% are freely accessible
[r]References35 refs✓Minimum 10 references required
[w]Words [REQ]2,819✓Minimum 2,000 words for a full research article. Current: 2,819
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18648784
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]3%✗≥60% of references from 2025–2026. Current: 3%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams5✓Mermaid architecture/flow diagrams. Current: 5
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (43 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)
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Anticipatory Intelligence: Gap Analysis — Exogenous Variable Integration in RNN Architectures

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Academic Research by Dmytro Grybeniuk & Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18648776  59stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources38%○≥80% from editorially reviewed sources
[t]Trusted93%✓≥80% from verified, high-quality sources
[a]DOI48%○≥80% have a Digital Object Identifier
[b]CrossRef34%○≥80% indexed in CrossRef
[i]Indexed31%○≥80% have metadata indexed
[l]Academic59%○≥80% from journals/conferences/preprints
[f]Free Access52%○≥80% are freely accessible
[r]References29 refs✓Minimum 10 references required
[w]Words [REQ]3,761✓Minimum 2,000 words for a full research article. Current: 3,761
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18648776
[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
[m]Diagrams4✓Mermaid architecture/flow diagrams. Current: 4
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (64 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)

Recurrent neural networks (LSTMs, GRUs) dominate time series forecasting but share a critical architectural limitation: external signals—weather forecasts, economic indicators, news sentiment—enter through the same processing pathway as historical target data, competing for representational capacity rather than receiving dedicated attention. This article examines the $176 billion annual cost of...

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Academic Research by Dmytro Grybeniuk & Oleh Ivchenko DOI: 10.5281/zenodo.18648776 59stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources38%○≥80% from editorially reviewed sources
[t]Trusted93%✓≥80% from verified, high-quality sources
[a]DOI48%○≥80% have a Digital Object Identifier
[b]CrossRef34%○≥80% indexed in CrossRef
[i]Indexed31%○≥80% have metadata indexed
[l]Academic59%○≥80% from journals/conferences/preprints
[f]Free Access52%○≥80% are freely accessible
[r]References29 refs✓Minimum 10 references required
[w]Words [REQ]3,761✓Minimum 2,000 words for a full research article. Current: 3,761
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18648776
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
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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]Diagrams4✓Mermaid architecture/flow diagrams. Current: 4
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (64 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)
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Anticipatory Intelligence: Anticipatory vs Reactive Systems — A Comparative Framework

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Academic Research by Dmytro Grybeniuk & Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18626628  53stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
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[t]Trusted89%✓≥80% from verified, high-quality sources
[a]DOI22%○≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed67%○≥80% have metadata indexed
[l]Academic22%○≥80% from journals/conferences/preprints
[f]Free Access89%✓≥80% are freely accessible
[r]References9 refs○Minimum 10 references required
[w]Words [REQ]3,273✓Minimum 2,000 words for a full research article. Current: 3,273
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18626628
[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 (54 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)

By Dmytro Grybeniuk, AI Architect | Anticipatory Intelligence Specialist | Stabilarity Hub | February 12, 2026

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Academic Research by Dmytro Grybeniuk & Oleh Ivchenko DOI: 10.5281/zenodo.18626628 53stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted89%✓≥80% from verified, high-quality sources
[a]DOI22%○≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed67%○≥80% have metadata indexed
[l]Academic22%○≥80% from journals/conferences/preprints
[f]Free Access89%✓≥80% are freely accessible
[r]References9 refs○Minimum 10 references required
[w]Words [REQ]3,273✓Minimum 2,000 words for a full research article. Current: 3,273
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18626628
[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 (54 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)
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Anticipatory Intelligence: State of the Art — Current Approaches to Predictive AI

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Academic Research by Dmytro Grybeniuk & Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18665635  64stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources34%○≥80% from editorially reviewed sources
[t]Trusted97%✓≥80% from verified, high-quality sources
[a]DOI63%○≥80% have a Digital Object Identifier
[b]CrossRef38%○≥80% indexed in CrossRef
[i]Indexed44%○≥80% have metadata indexed
[l]Academic63%○≥80% from journals/conferences/preprints
[f]Free Access63%○≥80% are freely accessible
[r]References32 refs✓Minimum 10 references required
[w]Words [REQ]3,171✓Minimum 2,000 words for a full research article. Current: 3,171
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18665635
[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
[m]Diagrams5✓Mermaid architecture/flow diagrams. Current: 5
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (72 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)

By Dmytro Grybeniuk, AI Architect | Anticipatory Intelligence Specialist | Stabilarity Hub | February 2026

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Academic Research by Dmytro Grybeniuk & Oleh Ivchenko DOI: 10.5281/zenodo.18665635 64stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources34%○≥80% from editorially reviewed sources
[t]Trusted97%✓≥80% from verified, high-quality sources
[a]DOI63%○≥80% have a Digital Object Identifier
[b]CrossRef38%○≥80% indexed in CrossRef
[i]Indexed44%○≥80% have metadata indexed
[l]Academic63%○≥80% from journals/conferences/preprints
[f]Free Access63%○≥80% are freely accessible
[r]References32 refs✓Minimum 10 references required
[w]Words [REQ]3,171✓Minimum 2,000 words for a full research article. Current: 3,171
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18665635
[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
[m]Diagrams5✓Mermaid architecture/flow diagrams. Current: 5
[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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Defining Anticipatory Intelligence: Taxonomy and Scope

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Academic Research by Dmytro Grybeniuk & Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18749471  70stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources48%○≥80% from editorially reviewed sources
[t]Trusted100%✓≥80% from verified, high-quality sources
[a]DOI96%✓≥80% have a Digital Object Identifier
[b]CrossRef61%○≥80% indexed in CrossRef
[i]Indexed17%○≥80% have metadata indexed
[l]Academic96%✓≥80% from journals/conferences/preprints
[f]Free Access43%○≥80% are freely accessible
[r]References23 refs✓Minimum 10 references required
[w]Words [REQ]3,208✓Minimum 2,000 words for a full research article. Current: 3,208
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18749471
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]4%✗≥60% of references from 2025–2026. Current: 4%
[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 (82 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)

In 2019, the U.S. Intelligence Community formally adopted "Anticipatory Intelligence" as a strategic priority, defining it as the ability to "sense, anticipate, and warn of emerging conditions, trends, threats, and opportunities that may require a rapid shift in national security posture, priorities, or emphasis" [1]. Yet when the same term appears in machine learning literature, healthcare inf...

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Academic Research by Dmytro Grybeniuk & Oleh Ivchenko DOI: 10.5281/zenodo.18749471 70stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources48%○≥80% from editorially reviewed sources
[t]Trusted100%✓≥80% from verified, high-quality sources
[a]DOI96%✓≥80% have a Digital Object Identifier
[b]CrossRef61%○≥80% indexed in CrossRef
[i]Indexed17%○≥80% have metadata indexed
[l]Academic96%✓≥80% from journals/conferences/preprints
[f]Free Access43%○≥80% are freely accessible
[r]References23 refs✓Minimum 10 references required
[w]Words [REQ]3,208✓Minimum 2,000 words for a full research article. Current: 3,208
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18749471
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]4%✗≥60% of references from 2025–2026. Current: 4%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
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The Black Swan Problem: Why Traditional AI Fails at Prediction

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Academic Research
Academic Research by Dmytro Grybeniuk & Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18749477  27stabilfr·wdophcgmx
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Traditional recurrent neural network architectures—including LSTMs and GRUs—exhibit systematic failure modes when confronted with Black Swan events: rare, high-impact occurrences that fall outside the training distribution. This technical analysis quantifies the economic impact of prediction failures, examines the mathematical foundations of why these architectures fail, and introduces the conc...

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Academic Research by Dmytro Grybeniuk & Oleh Ivchenko DOI: 10.5281/zenodo.18749477 27stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted43%○≥80% from verified, high-quality sources
[a]DOI0%○≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed14%○≥80% have metadata indexed
[l]Academic0%○≥80% from journals/conferences/preprints
[f]Free Access43%○≥80% are freely accessible
[r]References7 refs○Minimum 10 references required
[w]Words [REQ]0✗Minimum 2,000 words for a full research article. Current: 0
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18749477
[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%
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