Collapse Index (CI): A Domain-Agnostic Mathematical Framework for Interpreting Stellar Instability and Supernova Collapse
Bibliographic record
Abstract
This preprint introduces Collapse Index (CI): A Domain-Agnostic Mathematical Framework for Interpreting Stellar Instability and Supernova Collapse. The work demonstrates that the Collapse Index (CI), a bounded, lightweight instability metric originally developed for AI brittleness analysis, can quantify collapse structure across a synthetic Type II supernova lifecycle. Using a 600,000-row synthetic dataset generated with CI’s domain-agnostic perturbation engine, the study evaluates six canonical phases of stellar evolution: hydrostatic equilibrium, late-stage instability accumulation, catastrophic core collapse, shock rebound turbulence, plateau stabilization, and remnant cooling. CI successfully recovers the characteristic collapse arc (baseline → drift → critical spike → rebound → plateau → new equilibrium) without astrophysical tuning or domain-specific modeling. The framework is presented as a non-operational, conceptual tool for retrospective analysis only. CI is not a physical model and provides no predictive, detection, or early-warning capability. All astrophysical interpretation must be performed by accredited experts using archived datasets or simulations. Project page: https://collapseindex.org Licensed under CC BY-NC-ND 4.0.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".