A SPECTROSCOPIC ANALYSIS OF WHITE DWARFS IN THE KISO SURVEY
Bibliographic record
Abstract
We present a spectroscopic analysis of white dwarfs found in the Kiso survey. Spectroscopic observations at high signal-to-noise ratio have been obtained for all DA and DB stars in the Kiso Schmidt ultraviolet excess survey (KUV stars). These observations led to the reclassification of several KUV objects, including the discovery of three unresolved DA+DB double-degenerate binaries. The atmospheric parameters ( T eff and log g ) are obtained from detailed model atmosphere fits to optical spectroscopic data. The mass distribution of our sample is characterized by a mean value of 0.606 M ☉ and a dispersion of 0.135 M ☉ for DA stars, and 0.758 M ☉ and a dispersion of 0.192 M ☉ for DB stars. Absolute visual magnitudes obtained from our spectroscopic fits allow us to derive an improved luminosity function for the DA and DB stars identified in the Kiso survey. Our luminosity function is found to be significantly different from earlier estimates based on empirical photometric calibrations of M V for the same sample. The results for the DA stars now appear entirely consistent with those obtained for the PG survey using the same spectroscopic approach. The space density for DA stars with M V ⩽ 12.75 is 2.80 × 10 −4 pc −3 in the Kiso survey, which is 9.6% smaller than the value found in the PG survey. The completeness of both surveys is briefly discussed.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".