Coherence Efficiency Under Compression: Froggle's Dilemma, Blacksmith Magic, and Unified Channel Selection in Time-Scalar Field Theory
Bibliographic record
Abstract
We present a unified framework for compact-object phenomenology based on Time-Scalar Field Theory (TSFT), in which magnetism, radiation, thermalization, and gravitational dynamics emerge as efficiency-selected channels for resolving temporal shear under compression. Central to this framework is Froggle’s Dilemma: the principle that physical systems preferentially route shear through the lowest-cost coherent channel available in a given environment. We apply this framework to neutron stars and magnetars, deriving a TSFT stability score that combines rotational coherence, inferred magnetic shear, spin-down power, and characteristic age. Using publicly available pulsar timing data from the ATNF Pulsar Catalogue and magnetar data from the McGill Magnetar Catalog, we show that TSFT-motivated composite predictors outperform standard dipole-based heuristics in separating magnetars from ordinary pulsars, achieving a statistically significant improvement in classification performance. Within the magnetar population, the TSFT stability score exhibits strong correlations with observed X-ray luminosity, spectral index, and thermal properties, consistent with channel-selection predictions in which torsional coherence saturates and radiative export becomes dominant. These results demonstrate that magnetar phenomenology is governed not solely by magnetic field strength, but by coherence efficiency under compression. The framework provides a testable, extensible basis for multi-channel compact-object modeling and offers falsifiable predictions for transitions between electromagnetic, weak, and gravitational shear export regimes in extreme astrophysical environments, building on prior TSFT derivations of temporal shear, coherence saturation, and channel failure.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".