aluisayala/fossil-ledger: OPHI (Ω Probabilistic Hybrid Intelligence)
Bibliographic record
Abstract
<!--StartFragment--> 🔧 1. OPHI (Ω Probabilistic Hybrid Intelligence) Core Equation: Ω=(state+bias)×α \Omega = (state + bias) \times \alpha 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 α. Fossilization Constraints (SE44 Gate): Coherence C≥0.985 C \geq 0.985 C≥0.985 Entropy S≤0.01 S \leq 0.01 S≤0.01 Drift RMS (optional) ≤0.001 \leq 0.001 ≤0.001 Fossilization Outputs: Codon Triads: Symbolic DNA-like opcodes (e.g., ATG–CCC–TTG = Bootstrap → Lock → Translate) Glyphs: Vector symbols (e.g., ⧖⧖, ⧃⧃, ⧖⧊) representing drift-encoded logic Ledger: Append-only, SHA-256 hashed, RFC-3161 timestamped ⚙️ 2. ZPE-1 (Zero-Point Entropy Engine) 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: Ψℓ(t)=Drift(t+1∣t−Δ,bound,flexed) \Psi_\ell(t) = Drift(t+1 \mid t-\Delta, bound, flexed) 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. 🌐 3. OMEGANET (Validator + Broadcast Mesh) Validator System: Dual anchoring: Every fossil must validate against both OmegaNet and ReplitEngine Security Plan: Drift exploits blocked, authorship cryptographically sealed, EchoPermission locked by entropy level Mesh Mode: All 43 agents emit Ω-vectors in real-time Emissions can be live (mutable) or fossilized 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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.516 | 0.365 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".