<i>HERSCHEL</i>OBSERVATIONS OF THE W3 GMC: CLUES TO THE FORMATION OF CLUSTERS OF HIGH-MASS STARS
Bibliographic record
Abstract
The W3 GMC is a prime target for the study of the early stages of high-mass star formation. We have used Herschel data from the HOBYS key program to produce and analyze column density and temperature maps. Two preliminary catalogs were produced by extracting sources from the column density map and from Herschel maps convolved to 500 μm resolution. Herschel reveals that among the compact sources (FWHM < 0.45 pc), W3 East, W3 West, and W3 (OH) are the most massive and luminous and have the highest column density. Considering the unique properties of W3 East and W3 West, the only clumps with ongoing high-mass star formation, we suggest a "convergent constructive feedback" scenario to account for the formation of a cluster with decreasing age and increasing system/source mass toward the innermost regions. This process, which relies on feedback by high-mass stars to ensure the availability of material during cluster formation, could also lead to the creation of an environment suitable for the formation of Trapezium-like systems. In common with other scenarios proposed in other HOBYS studies, our results indicate that an active/dynamic process aiding in the accumulation, compression, and confinement of material is a critical feature of the high-mass star/cluster formation, distinguishing it from classical low-mass star formation. The environmental conditions and availability of triggers determine the form in which this process occurs, implying that high-mass star/cluster formation could arise from a range of scenarios: from large-scale convergence of turbulent flows to convergent constructive feedback or mergers of filaments.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".