Bibliographic record
Abstract
Abstract In this paper we analyze Sloan g, r, i archival imaging data of M33 taken by Hartman et al. using MegaCam at the Canada–France–Hawaii Telescope. To determine the distance to the M33 galaxy, we performed several analytical steps to identify its Cepheid population. We used the Lomb–Scargle algorithm to find periodicity and visually identified 1989 periodic variable stars. Since Cepheids occupy a specific region of the color–magnitude diagram, to differentiate Cepheids from other variables we used the expected position of the Cepheid instability strip to down-select Cepheids in M33 from other variables. This led to our sample of 1622 variables, the largest Cepheid sample known in M33 to date. We further classified these Cepheids into different subclasses, and used the fundamental mode Cepheids to estimate distance moduli for M33 in different filters: μ = 25.044 ± 0.083 mag in the g filter, μ = 24.886 ± 0.074 mag in the r filter, and μ = 24.785 ± 0.068 mag for the i filter. These results are in agreement with previous results.
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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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".