Candidate Water Vapor Lines to Locate the H<sub>2</sub>O Snowline through High-dispersion Spectroscopic Observations. III. Submillimeter H<sub>2</sub> <sup>16</sup>O and H<sub>2</sub> <sup>18</sup>O Lines
Bibliographic record
Abstract
Abstract In this paper, we extend the results presented in our former papers on using ortho- line profiles to constrain the location of the H2O snowline in T Tauri and Herbig Ae disks, to include submillimeter para- and ortho- and para- lines. Since the number densities of the ortho- and para- molecules are about 560 times smaller than their 16O analogs, they trace deeper into the disk than the ortho- lines (down to z = 0, i.e., the midplane). Thus these lines are potentially better probes of the position of the H2O snowline at the disk midplane, depending on the dust optical depth. The values of the Einstein A coefficients of submillimeter candidate water lines tend to be lower (typically <10−4 s−1) than infrared candidate water lines. Thus in the submillimeter candidate water line cases, the local intensity from the outer optically thin region in the disk is around 104 times smaller than that in the infrared candidate water line cases. Therefore, in the submillimeter lines, especially and para- lines with relatively lower upper state energies (∼a few 100 K) can also locate the position of the H2O snowline. We also investigate the possibility of future observations with ALMA to identify the position of the water snowline. There are several candidate water lines that trace the hot water gas inside the H2O snowline in ALMA Bands 5–10.
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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".