Toward a 3D kinetic tomography of Taurus clouds
Bibliographic record
Abstract
Context. Three-dimensional (3D) kinetic maps of the Milky Way interstellar medium are an essential tool in studies of its structure and of star formation. Aims. We aim to assign radial velocities to Galactic interstellar clouds now spatially localized based on starlight extinction and star distances from Gaia and stellar surveys. Methods. We developed an automated search for coherent projections on the sky of clouds isolated in 3D extinction density maps on the one hand, and regions responsible for CO radio emissions at specific Doppler shifts on the other hand. The discrete dust structures were obtained by application of the Fellwalker algorithm to a recent 3D extinction density map. For each extinction cloud, a technique using a narrow sliding spectral window moved along the contour-bounded CO spectrum and geometrical criteria was used to select the most likely velocity interval. Results. We applied the new contour-based technique to the 3D extinction density distribution within the volume encompassing the Taurus, Auriga, Perseus, and California molecular complexes. From the 45 clouds issued from the decomposition, 42 were assigned a velocity. The remaining structures correspond to very weak CO emission or extinction. We used the non-automated assignments of radial velocities to clouds of the same region presented in Paper I and based on KI absorption spectra as a validation test. The new fully automated determinations were found to be in good agreement with these previous measurements. Conclusions. Our results show that an automated search based on cloud-contour morphology can be efficient and that this novel technique may be extended to wider regions of the Milky Way and at larger distance. We discuss its limitations and potential improvements after combination with other techniques.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".