DISTRIBUTION AND CHARACTERISTICS OF INFRARED DARK CLOUDS USING GENETIC FORWARD MODELLING
Bibliographic record
Abstract
Infrared Dark Clouds (IRDCs) are dark clouds seen in silhouette in mid-infrared surveys. They are thought to be the birthplace of massive stars, yet remarkably little information exists on the properties of the population as a whole (e.g., mass spectrum, spatial distribution). Genetic forward modeling is used along with the Two Micron All Sky Survey and the Besançon Galactic model to deduce the three-dimensional distribution of interstellar extinction toward previously identified IRDC candidates. This derived dust distribution can then be used to determine the distance and mass of IRDCs, independently of kinematic models of the Milky Way. Along a line of sight that crosses an IRDC, the extinction is seen to rise sharply at the distance of the cloud. Assuming a dust-to-gas ratio, the total mass of the cloud can be estimated. The method has been successfully applied to 1259 IRDCs, including over 1000 for which no distance or mass estimate currently exists. The IRDCs are seen to lie preferentially along the spiral arms and in the molecular ring of the Milky Way, reinforcing the idea that they are the birthplace of massive stars. Also, their mass spectrum is seen to follow a power law with an index of −1.75 ± 0.06, steeper than giant molecular clouds (GMCs) in the inner Galaxy but comparable to clumps in GMCs. This slope suggests that the IRDCs detected using the present method are not gravitationally bound, but are rather the result of density fluctuations induced by turbulence.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".