The JCMT BISTRO Survey: The Distribution of Magnetic Field Strengths toward the OMC-1 Region
Bibliographic record
Abstract
Abstract Measurement of magnetic field strengths in a molecular cloud is essential for determining the criticality of magnetic support against gravitational collapse. In this paper, as part of the JCMT BISTRO survey, we suggest a new application of the Davis–Chandrasekhar–Fermi (DCF) method to estimate the distribution of magnetic field strengths in the OMC-1 region. We use observations of dust polarization emission at 450 and 850 μm, and C18O (3–2) spectral line data obtained with the JCMT. We estimate the volume density, the velocity dispersion, and the polarization angle dispersion in a box, 40″ × 40″ (5×5 pixels), which moves over the OMC-1 region. By substituting three quantities in each box with the DCF method, we get magnetic field strengths over the OMC-1 region. We note that there are very large uncertainties in the inferred field strengths, as discussed in detail in this paper. The field strengths vary from 0.8 to 26.4 mG, and their mean value is about 6 mG. Additionally, we obtain maps of the mass-to-flux ratio in units of a critical value and the Alfvén Mach number. The central parts of the BN–KL and South (S) clumps in the OMC-1 region are magnetically supercritical, so the magnetic field cannot support the clumps against gravitational collapse. However, the outer parts of the region are magnetically subcritical. The mean Alfvén Mach number is about 0.4 over the region, which implies that the magnetic pressure exceeds the turbulent pressure in the OMC-1 region.
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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 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".