Bibliographic record
Abstract
The 5-year project Supernova Legacy Survey (SNLS) delivers $\sim 100$ Type-Ia supernovae (SNe Ia) per year, in the redshift range $0.3 < z < 1.0$, with well-sampled $g'r'i'z'$ lightcurves. The SNLS Collaboration uses the 1 deg$^2$ Megacam imager (36 $2048 \times 4612$ thinned CCDs) mounted on the 3.6-m Canada-France-Hawaii Telescope (CFHT) to observe four fields around the sky, in four filters. The primary goal of the project is to measure the dark energy equation of state with a final statistical precision of $\pm 0.05$. We have shown, using the first year dataset that the calibration uncertainties are currently the dominant contribution to the systematic error budget. The calibration of the SNLS dataset is challenging in several aspects. First, Megacam is a wide-field imager, and only a handful of its 36 CCDs can be directly calibrated using standard star observations. Second, measuring the rest-frame $B$-band luminosity of SNe Ia over the $0.3
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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".