The Pan-STARRS1 Small Area Survey 2
Bibliographic record
Abstract
The Panoramic Survey Telescope and Rapid Response System 1 (Pan-STARRS1) survey is acquiring multi-epoch imaging in five bands (gP1, rP1, iP1, zP1, yP1) over the entire sky north of declination −30° (the 3π survey). In 2011 July a test area of about 70 deg2 was observed to the expected final depth of the main survey. In this, the first of a series of papers targeting the galaxy count and clustering properties of the combined multi-epoch test area data, we present a detailed investigation into the depth of the survey and the reliability of the Pan-STARRS1 analysis software. We show that the Pan-STARRS1 reduction software can recover the properties of fake sources, and show good agreement between the magnitudes measured by Pan-STARRS1 and those from Sloan Digital Sky Survey. We also examine the number of false detections apparent in the Pan-STARRS1 data. Our comparisons show that the test area survey is somewhat deeper than the Sloan Digital Sky Survey in all bands, and, in particular, the z band approaches the depth of the stacked Sloan Stripe 82 data.
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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 | 0.001 | 0.002 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.021 | 0.014 |
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".