Bibliographic record
Abstract
L134N is a cold, starless cloud, very high above the galactic plane, close to us and well delineated in continuum dust emission maps. This cloud is considered to be representative of oxygen rich dark clouds (with the presence of SO, SO2, NO, ...). It is thus a good reference together with TMC-1 to test astrochemical models. Thanks to ISO, SCUBA and near IR wide field cameras, the detailed study of the dust has become possible in such cold and dark clouds. In parallel, progress in radiotelescope receiver sensitivity now allows to map weak lines on large surfaces. We have thus started a project to study both dust and a few gaseous key species (CO, CS, SO and N2H+) to address several questions. We want to assess the quantity of dust and gas all over the cloud, study possible C18O and/or C17O depletion towards dense cores, evaluate the structure of the gas, the abundance of CS and SO to possibly estimate the chemical age of the cloud (time dependent models show that the CS/SO ratio diminishes with time) and evaluate the rare isotope abundances, especially 17O and 34S in a first step. To constrain the molecular abundances with the highest possible confidence, we have observed several transitions for each species and each isotopomer. Though we have observed far less species than Dickens et al. [1], we have done it on a larger area, including thus the strongest C18O peak and two other peaks, with a better signal-to-noise ratio. Most of the data are already acquired. We present here preliminary results.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.067 | 0.019 |
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".