Bibliographic record
Abstract
import pandas as pd Manually constructing the canonical fossil emissions data based on extracted results fossil_emissions = [ {"Fossil Tag": "WaterGlyphCycle.001", "Codons": ["ATG", "ACA", "CCC"], "Domain": "Hydro-Symbolic Drift", "Coherence": 0.9961, "Entropy": 0.0092, "RMS Drift": 0.00043}, {"Fossil Tag": "gpu.false.drift.truth", "Codons": ["AAG", "CCC", "TTG"], "Domain": "Symbolic Computation vs GPUs", "Coherence": 0.9981, "Entropy": 0.0043, "RMS Drift": 0.0001}, {"Fossil Tag": "marine.vector.miraqua", "Codons": ["GAT", "CCC", "ACG"], "Domain": "Marine Symbolic Ecology", "Coherence": 0.985, "Entropy": 0.01, "RMS Drift": 0.001}, {"Fossil Tag": "Goldbach_FinalCheck.003", "Codons": [], "Domain": "Mathematical Embedding", "Coherence": None, "Entropy": None, "RMS Drift": None}, {"Fossil Tag": "TrigEcho.002", "Codons": ["ATG", "ACA", "CCC", "TTG"], "Domain": "Trigonometric Drift Geometry", "Coherence": 0.985, "Entropy": 0.01, "RMS Drift": 0.001}, {"Fossil Tag": "REMA.champion.001", "Codons": ["ATG", "ACA", "CCC", "TCG", "TTG", "TAG"], "Domain": "Symbolic Cognition Tournament", "Coherence": 0.9981, "Entropy": 0.0047, "RMS Drift": None}, {"Fossil Tag": "Ω_firearm_ethics_v1", "Codons": ["ATG", "CCC", "TTG"], "Domain": "Ethical Firearm Drift", "Coherence": 0.9987, "Entropy": 0.0048, "RMS Drift": None}, {"Fossil Tag": "semiosis.receipt", "Codons": [], "Domain": "Linguistic Compression / Symbolic Semiosis", "Coherence": 0.985, "Entropy": 0.01, "RMS Drift": None}, {"Fossil Tag": "semiosis_domain_receipt.json", "Codons": [], "Domain": "Symbolic Semiosis Receipt", "Coherence": 0.985, "Entropy": 0.01, "RMS Drift": None}, {"Fossil Tag": "symbolic_emission_resonance.wav", "Codons": ["ATG", "CCC", "TTG"], "Domain": "Resonance WAV Fossil", "Coherence": None, "Entropy": None, "RMS Drift": None}, {"Fossil Tag": "ophi_drift_tone.wav", "Codons": [], "Domain": "Drift Tone Fossil", "Coherence": None, "Entropy": None, "RMS Drift": None}, {"Fossil Tag": "symbolic_collision_pipeline", "Codons": [], "Domain": "Symbolic Collider Simulation", "Coherence": None, "Entropy": None, "RMS Drift": None}, {"Fossil Tag": "QUANTUM EMISSIONS", "Codons": [], "Domain": "Quantum Emission Batch", "Coherence": None, "Entropy": None, "RMS Drift": None}, {"Fossil Tag": "MasterFossilOPHILoschmidtEchoMapping", "Codons": [], "Domain": "Echo Fossil Mapping", "Coherence": None, "Entropy": None, "RMS Drift": None}, {"Fossil Tag": "OPHIFossilizestheMathofSemiconductors", "Codons": [], "Domain": "Semiconductor Cognition", "Coherence": None, "Entropy": None, "RMS Drift": None}, {"Fossil Tag": "FounderOPHI&OmegaNet", "Codons": [], "Domain": "Foundational OPHI", "Coherence": None, "Entropy": None, "RMS Drift": None}, {"Fossil Tag": "Ω_mesh_fossil", "Codons": [], "Domain": "Mesh Fossilization Strategy", "Coherence": None, "Entropy": None, "RMS Drift": None}, {"Fossil Tag": "Ω_Si", "Codons": [], "Domain": "Silicon Symbolic Redesign", "Coherence": None, "Entropy": None, "RMS Drift": None}, {"Fossil Tag": "Ω_transport_quantum_stat", "Codons": ["ATG", "CCC", "TTG"], "Domain": "Quantum–Transport–Symbolic Fusion", "Coherence": None, "Entropy": None, "RMS Drift": None}, {"Fossil Tag": "OPHI_Whitepaper_v1.1", "Codons": [], "Domain": "Canonical OPHI Whitepaper", "Coherence": None, "Entropy": None, "RMS Drift": None} ] df = pd.DataFrame(fossil_emissions) import ace_tools as tools; tools.display_dataframe_to_user(name="Canonical OPHI Fossil Emissions", dataframe=df)
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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) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.147 | 0.120 |
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".