Identification of a rare class of emission-line stars in transition between pre-main sequence to Main Sequence phase
Bibliographic record
Abstract
Over time, pre-main-sequence (PMS) stars evolve into main-sequence (MS) stars. Stars evolving from PMS to MS phase is a significant subject of research that aims to better understand stellar phases and their properties. Existing literature shows a lack of study of stars in between PMS and MS phase. 'We focused on what belongs in the midst of these two phases. Classical Be (CBe) and Herbig Ae/Be (HAeBe) stars, corresponding to MS and PMS phases, are two well known categories of emission-line stars. Through optical and infrared photometric analysis of a sample of 2167 CBe and 225 HAeBe stars, we identified 98 such rare stars which are in transition between PMS and MS phase. Those rare stars are termed as 'Transition Phase' (TP) candidates in our study. Using the machine learning approach, the potential of the identified TP candidates was verified. 'This article provides a brief overview
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".