MID-J CO SHOCK TRACING OBSERVATIONS OF INFRARED DARK CLOUDS. III. SLED FITTING
Bibliographic record
Abstract
ABSTRACT Giant molecular clouds contain supersonic turbulence that can locally heat small fractions of gas to over 100 K. We run shock models for low-velocity, C-type shocks propagating into gas with densities between 103 and 105 cm−3 and find that CO lines are the most important cooling lines. Comparison to photodissociation region (PDR) models indicates that mid-J CO lines (J = 8 7 and higher) should be dominated by emission from shocked gas. In Papers I and II we presented CO J = 3 2, 8 7, and 9 8 observations toward four primarily quiescent clumps within infrared dark clouds. Here we fit PDR models to the combined spectral line energy distributions and show that the PDR models that best fit the low-J CO emission underpredict the mid-J CO emission by orders of magnitude, strongly hinting at a hot gas component within these clumps. The low-J CO data clearly show that the integrated intensities of both the CO J = 8 7 and 9 8 lines are anomalously high, such that the line ratio can be used to characterize the hot gas component. Shock models are reasonably consistent with the observed mid-J CO emission, with models with densities near cm−3 providing the best agreement. Where this mid-J CO is detected, the mean volume filling factor of the hot gas is 0.1%. Much of the observed mid-J CO emission, however, is also associated with known protostars and may be due to protostellar feedback.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".