Transition from coherent cores to surrounding cloud in L1688
Bibliographic record
Abstract
Context. Stars form in cold dense cores showing subsonic velocity dispersions. The parental molecular clouds display higher temperatures and supersonic velocity dispersions. The transition from core to cloud has been observed in velocity dispersion, but temperature and abundance variations are unknown. Aims. We aim to measure the temperature and velocity dispersion across cores and ambient cloud in a single tracer to study the transition between the two regions. Methods. We use NH3 (1,1) and (2,2) maps in L1688 from the Green Bank Ammonia Survey, smoothed to 1′, and determine the physical properties by fitting the spectra. We identify the coherent cores and study the changes in temperature and velocity dispersion from the cores to the surrounding cloud. Results. We obtain a kinetic temperature map extending beyond dense cores and tracing the cloud, improving from previous maps tracing mostly the cores. The cloud is 4–6 K warmer than the cores, and shows a larger velocity dispersion (Δσv = 0.15–0.25 km s−1). Comparing to Herschel-based dust temperatures, we find that cores show kinetic temperatures that are ≈1.8 K lower than the dust temperature, while the gas temperature is higher than the dust temperature in the cloud. We find an average p-NH3 fractional abundance (with respect to H2) of (4.2 ± 0.2) × 10−9 towards the coherent cores, and (1.4 ± 0.1) × 10−9 outside the core boundaries. Using stacked spectra, we detect two components, one narrow and one broad, towards cores and their neighbourhoods. We find the turbulence in the narrow component to be correlated with the size of the structure (Pearson-r = 0.54). With these unresolved regional measurements, we obtain a turbulence–size relation of σv,NT ∝ r0.5, which is similar to previous findings using multiple tracers. Conclusions. We discover that the subsonic component extends up to 0.15 pc beyond the typical coherent boundaries, unveiling larger extents of the coherent cores and showing gradual transition to coherence over ~0.2 pc.
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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".