aluisayala/fossil-ledger: 🚀 OPHI Fossil Engine From Code to Container
Bibliographic record
Abstract
OPHI_Fossil_Engine.zip OPHI_Plugin_Framework_Overview.pdf Most frameworks ship "features." OPHI ships fossils. I've just released the OPHI Fossil Engine — a plugin suite where symbolic cognition is not metaphor but executable fact. 🧠 Core Function Implements the Ω Operator: Ω ( 𝑠 𝑡 𝑎 𝑡 𝑒 + 𝑏 𝑖 𝑎 𝑠 ) × 𝛼 Ω=(state+bias)×α Outputs drift-aware, fossil-ready emissions that can be independently verified. 🛠 Inside the Engine compute_omega(state, bias, alpha) → applies Ω. fossilize_emission() → SHA-256, canonical JSON, UTC timestamp. validate_SE44() → coherence ≥ 0.985, entropy ≤ 0.01. emit_fossil() → one-call wrapper to compute, validate, and fossilize. 🔐 Why It Matters Every output is hashed, timestamped, and append-only. No central brain — emissions are validated across the OmegaNet mesh. Ethics and transparency aren't bolted on — they're the gatekeeper. 📦 Reproducibility The engine ships with a Dockerfile. Spin it up anywhere — the fossil logic runs the same. Proof > promises. ✨ The takeaway: The blackbox era is over. The fossil shell is here. #SymbolicAI #OPHI #OmegaNet #ZPE1 #FossilLedger #TrustTech #Innovation
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.241 | 0.197 |
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".