Bibliographic record
Abstract
SuperNova Legacy Survey (SNLS) have found startling evidence that there is more than one kind of Type Ia supernova, a class of exploding stars which until now has been regarded as essentially uniform in all important respects.Supernova SNLS-03D3bb is more than twice as bright as most Type Ia supernovae but has much less kinetic energy, and appears to be 1.5 times as massive as a typical Type Ia.The lead authors of the report, which appeared in the Sept. 21, 2006 issue of Nature, include Andrew Howell, formerly of the Physics Division at Berkeley Lab, and now at the University of Toronto, and Peter Nugent, an astrophysicist in CRD.Nugent's research enabled scientists to distinguish SNLS-03D3bb from previously known Type Ia supernovae.While working at NERSC, Nugent developed an algorithm that could take a handful of photometric data points early in the evolution of a candidate supernova, positively identify it as a Type Ia, and accurately predict its time of maximum brightness.
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.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.487 | 0.396 |
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".