Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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)
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.248 | 0.108 |
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 it