Star formation in massive protoclusters in the Monoceros OB1 dark cloud
Bibliographic record
Abstract
We present far-infrared, submillimetre and millimetre observations of bright IRAS sources and outflows that are associated with massive CS clumps in the Monoceros OB1 dark cloud. Individual star-forming cores are identified within each clump. We show that combining submillimetre maps, obtained with SCUBA on the James Clerk Maxwell Telescope, with HIRES-processed and modelled IRAS data is a powerful technique that can be used to place better limits on individual source contributions to the far-infrared flux in clustered regions. Three previously categorized ‘Class I objects’ are shown to consist of multiple sources in different evolutionary stages. In each case, the IRAS point source dominates the flux at 12 and 25 μm. In two cases, the IRAS point source is not evident at submillimetre wavelengths. The submillimetre sources contribute significantly to the 60- and 100-μm fluxes, dominating the flux in the 100-μm waveband. Using fluxes derived from our technique, we present the spectral energy distribution and physical parameters for an intermediate-mass Class 0 object in one of the regions. Our new CO J= 2 → 1 outflow maps of the three regions studied indicate complex morphology suggestive of multiple driving sources. We discuss the possible implications of our results for published correlations between outflow momentum deposition rates and ‘source’ luminosities, and for using these derived properties to estimate the ratio of mass ejection rates to mass accretion rates on to protostars.
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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 | 0.000 |
| 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".