Bibliographic record
Abstract
Main changes in this update include: Add several new MOTS properties (several shear properties, invariant angle zeta) Add several new MOTT properties (evolution vector, xi vector, surface gravity, extremality parameter, slowness parameter) Rewrite MOTT signature computation Update documentation and added new tutorials The notion of "MOTT" properties refers to quantities that cannot be computed from knowledge of a single MOTS. Instead, we need at least an infinitesimal section of the world tube traced out by the MOTSs in time. These properties are hence computed using separate functions which can be called once part of the world tube has been found. In addition to the above, many smaller changes increase accuracy for certain calculations (e.g. mapping between proper length and numerical parametrisation), speed (e.g. by porting Chebyshev evaluation to C), convenience (e.g. for plotting), and robustness (e.g. atomic saves even over NFS). Research at Perimeter Institute is supported in part by the Government of Canada through the Department of Innovation, Science and Economic Development Canada and by the Province of Ontario through the Ministry of Colleges and Universities. We also thank the French EIPHI Graduate School (ANR-17-EURE-0002) and the Spanish FIS2017-86497-C2-1 project (with FEDER contribution) for support.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.535 | 0.601 |
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".