MULTI-SCALE ANALYSIS OF MAGNETIC FIELDS IN FILAMENTARY MOLECULAR CLOUDS IN ORION A
Bibliographic record
Abstract
New visible and K-band polarization measurements of stars surrounding molecular clouds in Orion A and stars in the Becklin–Neugebauer (BN) vicinity are presented. Our results confirm that magnetic fields located inside the Orion A molecular clouds and in their close neighborhood are spatially connected. On and around the BN object, we measured the angular offsets between the K-band polarization data and available submillimeter (submm) data. We find high values of the polarization degree, PK, and of the optical depth, τK, close to an angular offset position of 90° whereas lower values of PK and τK are observed for smaller angular offsets. We interpret these results as evidence for the presence of various magnetic field components toward lines of sight in the vicinity of BN. On a larger scale, we measured the distribution of angular offsets between available H-band polarization data and the same submm data set. Here we find an increase of 〈PH〉 with angular offset, which we interpret as a rotation of the magnetic field by ≲ 60°. This trend generalizes previous results on small scales toward and around lines of sight to BN and is consistent with a twist of the magnetic field on a larger scale toward OMC-1. A comparison of our results with several other studies suggests that a two-component magnetic field, perhaps helical, could be wrapping the OMC-1 filament.
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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 | 0.001 | 0.001 |
| 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".