A Genetic Algorithm-Based Exploration of Three Filament Models: A Case for the Magnetic Support of the G11.11-0.12 Infrared-Dark Cloud
Bibliographic record
Abstract
The G11.11-0.12 infrared-dark cloud has a filamentary appearance, both in extinction against the diffuse infrared emission of the Galactic plane and in emission at 850 microns. We use a novel computational technique based on an advanced genetic algorithm to explore thoroughly 3 different models of self-gravitating, pressure truncated filaments and to constrain their parameters. Specifically, the models tested are the non-magnetic Ostriker (1964) model, a generalized version of the magnetic Stodolkiewicz (1963) model, and the magnetic Fiege & Pudritz (2000) model. Previous results showed that G11.11-0.12 has a much steeper r^{-4} radial density profile than other filaments, where the density varies approximately as r^{-2}, and that this steep density profile is consistent with the Ostriker (1964) model. We present a more complete analysis that shows that the radial structure of G11.11-0.12 is consistent with regimes of each of these models. All of the magnetic models that agree with the data are threaded by a dominant poloidal magnetic field, and most have dynamically significant fields. Thus, G11.11-0.12 is an excellent candidate for radial support by a magnetic field that is predominantly poloidal. We predict the polarization patterns expected for both magnetic models and show that the two magnetic models produce different polarization patterns that should be distingished by observations.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".