Dust and the art of Galactic map making
Bibliographic record
Abstract
Abstract A Galactic model of stellar population synthesis is used along with a genetic algorithm to reconstruct the three dimensional dust distribution in the Milky Way. We have applied this technique towards over 1500 IRDC cloud candidates, for which we recovered distances and masses for 1259 of them. Aside from giving us the distance to the dust, the three dimensional extinction map also provides us with a temperature independent measure of its density. This new method is independent of any kinematical information, thus providing a new way to obtain information on the Galactic distribution of the ISM. It is a good complement to existing measures which are solely based on molecular gas kinematics as both methods are completely independent and both are affected by different systematics. It will be able to provide valuable distance information for use in the analysis and interpretation of far-infrared and sub-millimetre observations by Herschel and Planck. In the future it could be used with deeper stellar observations or observations at longer wavelengths in order to probe even higher density clouds and to even larger distances.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".