Two mass distributions in the L 1641 molecular clouds: the <i>Herschel</i> connection of dense cores and filaments in Orion A
Bibliographic record
Abstract
We present Herschel survey maps of the L 1641 molecular clouds in Orion A. We extracted both the filaments and dense cores in the region. We identified which of the dense sources are proto- or pre-stellar, and studied their association with the identified filaments. We find that although most (71%) of the pre-stellar sources are located on filaments there, is still a significant fraction of sources not associated with such structures. We find that these two populations (on and off the identified filaments) have distinctly different mass distributions. The mass distribution of the sources on the filaments is found to peak at 4 M ⊙ and drives the shape of the core mass function (CMF) at higher masses, which we fit with a power law of the form dN/dlogM∝M -1.4 ± 0.4. The mass distribution of the sources off the filaments, on the other hand, peaks at 0.8 M ⊙ and leads to a flattening of the CMF at masses lower than ~4 M ⊙. We postulate that this difference between the mass distributions is due to the higher proportion of gas that is available in the filaments, rather than in the diffuse cloud. \n \nHerschel is an ESA space observatory with science instruments provided by European-led Principal Investigator consortia and with important participation from NASA.
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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 | 0.000 | 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 | 0.000 |
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".