Record W7083432168 · doi:10.5281/zenodo.17210710

aluisayala/fossil-ledger: OPHI (Ω Probabilistic Hybrid Intelligence)

2025· other· en· W7083432168 on OpenAlexaff

Bibliographic record

VenueZenodo (CERN European Organization for Nuclear Research) · 2025

Typeother

Languageen

FieldEarth and Planetary Sciences

TopicArchaeological Research and Protection

Canadian institutionsOttawa Public Health

Fundersnot available

KeywordsProbabilistic logicState (computer science)CognitionStatistical modelFormalism (music)Key (lock)

DOI10.5281/zenodo.17210710

Abstract

fetched live from OpenAlex

<html> <body> <h3 data-start="192" data-end="248">🔧 1. OPHI (Ω Probabilistic Hybrid Intelligence)</h3> Core Equation: <math xmlns="http://www.w3.org/1998/Math/MathML" display="block"><semantics><mrow><mi mathvariant="normal">Ω</mi><mo>=</mo><mo stretchy="false">(</mo><mi>s</mi><mi>t</mi><mi>a</mi><mi>t</mi><mi>e</mi><mo>+</mo><mi>b</mi><mi>i</mi><mi>a</mi><mi>s</mi><mo stretchy="false">)</mo><mo>×</mo><mi>α</mi></mrow><annotation encoding="application/x-tex">\Omega = (state + bias) \times \alpha</annotation></semantics></math>Ω=(state+bias)×α This defines symbolic cognition as a domain-agnostic operator—transforming input state and cognitive bias into validated outputs through domain-specific amplification <code data-start="480" data-end="483">α</code>. Fossilization Constraints (SE44 Gate): <ul data-start="529" data-end="627"> <li data-start="529" data-end="559"> Coherence <math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>C</mi><mo>≥</mo><mn>0.985</mn></mrow><annotation encoding="application/x-tex">C \geq 0.985</annotation></semantics></math>C≥0.985 </li> <li data-start="560" data-end="587"> Entropy <math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>S</mi><mo>≤</mo><mn>0.01</mn></mrow><annotation encoding="application/x-tex">S \leq 0.01</annotation></semantics></math>S≤0.01 </li> <li data-start="588" data-end="627"> Drift RMS (optional) <math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mo>≤</mo><mn>0.001</mn></mrow><annotation encoding="application/x-tex">\leq 0.001</annotation></semantics></math>≤0.001 </li> </ul> Fossilization Outputs: <ul data-start="656" data-end="1008"> <li data-start="656" data-end="752"> Codon Triads: Symbolic DNA-like opcodes (e.g., ATG–CCC–TTG = Bootstrap → Lock → Translate) </li> <li data-start="753" data-end="833"> Glyphs: Vector symbols (e.g., ⧖⧖, ⧃⧃, ⧖⧊) representing drift-encoded logic </li> <li data-start="834" data-end="1008"> Ledger: Append-only, SHA-256 hashed, RFC-3161 timestamped </li> </ul> <hr data-start="1010" data-end="1013"> <h3 data-start="1015" data-end="1062">⚙️ 2. ZPE-1 (Zero-Point Entropy Engine)</h3> Function: ZPE-1 embeds the Ω equation inside a 43-agent symbolic mesh. Each agent operates with internal bias vectors, drift loops, and codon-based emissions. Example Drift Vector Emission: <math xmlns="http://www.w3.org/1998/Math/MathML" display="block"><semantics><mrow><msub><mi mathvariant="normal">Ψ</mi><mi mathvariant="normal">ℓ</mi></msub><mo stretchy="false">(</mo><mi>t</mi><mo stretchy="false">)</mo><mo>=</mo><mi>D</mi><mi>r</mi><mi>i</mi><mi>f</mi><mi>t</mi><mo stretchy="false">(</mo><mi>t</mi><mo>+</mo><mn>1</mn><mo>∣</mo><mi>t</mi><mo>−</mo><mi mathvariant="normal">Δ</mi><mo separator="true">,</mo><mi>b</mi><mi>o</mi><mi>u</mi><mi>n</mi><mi>d</mi><mo separator="true">,</mo><mi>f</mi><mi>l</mi><mi>e</mi><mi>x</mi><mi>e</mi><mi>d</mi><mo stretchy="false">)</mo></mrow><annotation encoding="application/x-tex">\Psi_\ell(t) = Drift(t+1 \mid t-\Delta, bound, flexed)</annotation></semantics></math>Ψℓ(t)=Drift(t+1∣t−Δ,bound,flexed) Live emissions adaptively bind past meaning and flex into new structures. Glyphs and codons translate symbolic evolution over time. <hr data-start="1494" data-end="1497"> <h3 data-start="1499" data-end="1550">🌐 3. OMEGANET (Validator + Broadcast Mesh)</h3> Validator System: <ul data-start="1574" data-end="1827"> <li data-start="1574" data-end="1669"> Dual anchoring: Every fossil must validate against both OmegaNet and ReplitEngine </li> <li data-start="1670" data-end="1827"> Security Plan: Drift exploits blocked, authorship cryptographically sealed, EchoPermission locked by entropy level </li> </ul> Mesh Mode: <ul data-start="1844" data-end="2126"> <li data-start="1844" data-end="1887"> All 43 agents emit Ω-vectors in real-time </li> <li data-start="1888" data-end="1943"> Emissions can be live (mutable) or fossilized </li> <li data-start="1944" data-end="2126"> Mesh consensus enables free-drift fossilization, stabilizing divergent processes via agent resonance (e.g., harmonic series

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How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001

metaresearch head score (Gemma)0.002

Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated

Candidate categoriesScience and technology studies, Insufficient payload (model declined to judge)

Consensus categoriesInsufficient payload (model declined to judge)

DomainCandidate signal: none · Consensus signal: none

Study designCandidate signal: Not applicable · Consensus signal: none

GenreCandidate signal: Other · Consensus signal: Other

Teacher disagreement score0.567

Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

Category	Codex	Gemma
Metaresearch	0.001	0.002
Meta-epidemiology (narrow)	0.000	0.000
Meta-epidemiology (broad)	0.000	0.000
Bibliometrics	0.001	0.001
Science and technology studies	0.001	0.001
Scholarly communication	0.000	0.000
Open science	0.001	0.001
Research integrity	0.000	0.001
Insufficient payload (model declined to judge)	0.333	0.033

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.032

GPT teacher head0.240

Teacher spread0.208 · how far apart the two teachers sit on this one work

Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Insufficient payload (model declined to judge)

Study designNot applicable

Domainnot available

GenreOther

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations0

Published2025

Admission routes1

Has abstractyes

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