Bibliographic record
Abstract
The description looks perfect. That's exactly the abstract from the paper. You can optionally add a bit more detail after the abstract if you want. Here's an enhanced version you could use instead (or keep what you have—it's already good): Enhanced Description (optional): The Fractal Resonance Cognition (FRC) framework proposes that quantum measurement arises from deterministic phase-locking to coherence attractors, with the Born rule emerging as an equilibrium distribution rather than a fundamental postulate. This paper addresses ten foundational questions that arise naturally from the framework: the ontological status of the coherence field, the origin of the drift term, relativistic consistency, the treatment of identical particles, the relationship to decoherence, and experimental signatures that distinguish FRC from standard quantum mechanics. We provide mathematically precise resolutions grounded in information geometry and open-system dynamics, identify the controlled limits where the framework becomes exact, and propose concrete experimental protocols. This document serves as a companion to the FRC 100-series and 566-series, consolidating interpretive choices and anticipated concerns. Key topics addressed: - Ontological status of the Lambda-field (effective vs. fundamental) - Bures-metric gradient flow derivation of the drift term - Relativistic formulation via local sources - Born rule from coherence equilibration - Experimental discriminators: variance scaling, velocity autocorrelation - Comparison to Copenhagen, Many-Worlds, Bohm, and GRW interpretations Includes 4 figures illustrating gradient flow dynamics, measurement timeline, experimental predictions, and interpretation comparison.
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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 | 0.005 |
| 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 | 0.006 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".