An Exploration of Tensor Virial Equilibrium in Magnetized Molecular Cloud Cores
Bibliographic record
Abstract
Abstract PolCat is a software package designed to generate and constrain models of triaxial, magnetized molecular cloud cores using submillimeter polarization and continuum intensity data. Such models often compare well with observational data, but they are mathematically degenerate. As a result, many allowed models are either very elongated or flattened, and far from virial equilibrium. We present a tensor virial analysis of PolCat models with the aim of developing an optional new regularization constraint that can be used on the fly during the PolCat modeling process. This constraint is intended to guide PolCat toward models that are in or close to equilibrium on all three principal axes. While we have found the expected families of spheroidal solutions in tensor virial balance, we have also found a population of triaxial cores that are in three-axis tensor virial equilibrium. We find that models generated using the virial constraint have much more realistic shapes, with very elongated or flattened solutions now absent. Thus, these show that the tensor virial constraint may be useful for restricting PolCat’s solution space to more realistic models, thereby also reducing the degeneracy inherent in our approach. We also perform a Monte Carlo analysis to predict the range of projected axis ratios present in the population of equilibrium solutions to suggest how these cores may appear when observed.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".