STAR FORMATION AND YOUNG STELLAR CONTENT IN THE W3 GIANT MOLECULAR CLOUD
Bibliographic record
Abstract
In this work, we have carried out an in-depth analysis of the young stellar content in the W3 giant molecular cloud (GMC). The young stellar object (YSO) population was identified and classified in the Infrared Array Camera/Multiband Imaging Photometer color–magnitude space according to the "Class" scheme and compared to other classifications based on intrinsic properties. Class 0/I and II candidates were also compared to low-/intermediate-mass pre-main-sequence (PMS) stars selected through their colors and magnitudes in the Two Micron All Sky Survey. We find that a reliable color/magnitude selection of low-mass PMS stars in the infrared requires prior knowledge of the protostar population, while intermediate-mass objects can be more reliably identified. By means of the minimum spanning tree algorithm and our YSO spatial distribution and age maps, we investigated the YSO groups and the star formation history in W3. We find signatures of clustered and distributed star formation in both triggered and quiescent environments. The central/western parts of the GMC are dominated by large-scale turbulence likely powered by isolated bursts of star formation that triggered secondary star formation events. Star formation in the eastern high-density layer (HDL) also shows signs of quiescent and triggered stellar activity, as well as extended periods of star formation. While our findings support triggering as a key factor for inducing and enhancing some of the major star-forming activity in the HDL (e.g., W3 Main/W3(OH)), we argue that some degree of quiescent or spontaneous star formation is required to explain the observed YSO population. Our results also support previous studies claiming a spontaneous origin for the isolated massive star(s) powering KR 140.
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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 | 0.001 | 0.001 |
| 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) | 0.001 | 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".