Water abundances in high-mass protostellar envelopes:<i>Herschel</i>observations with HIFI
Bibliographic record
Abstract
Aims. We derive the dense core structure and the water abundance in four massive star-forming regions in the hope of understanding the earliest stages of massive star formation.Methods. We present Herschel/HIFI observations of the para-H2O 111–000 and 202–111 and the para-H218O 111–000 transitions. The envelope contribution to the line profiles is separated from contributions by outflows and foreground clouds. The envelope contribution is modeled with Monte-Carlo radiative transfer codes for dust and molecular lines (MC3D and RATRAN), and the water abundance and the turbulent velocity width as free parameters.Results. While the outflows are mostly seen in emission in high-J lines, envelopes are seen in absorption in ground-state lines, which are almost saturated. The derived water abundances range from 5×10-10 to 4×10-8 in the outer envelopes. We detect cold clouds surrounding the protostar envelope, thanks to the very high quality of the Herschel/HIFI data and the unique ability of water to probe them. Several foreground clouds are also detected along the line of sight.Conclusions. The low H2O abundances in massive dense cores are in accordance with the expectation that high densities and low temperatures lead to freeze-out of water on dust grains. The spread in abundance values is not clearly linked to physical properties of the sources.
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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 | 0.000 | 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".