Bibliographic record
Abstract
We present the first data release of the ALMA-IMF Large Program, which covers the 12m-array continuum calibration and imaging. The ALMA-IMF Large Program is a survey of fifteen dense molecular cloud regions spanning a range of evolutionary stages that aims to measure the core mass function (CMF). We describe the data acquisition and calibration done by the Atacama Large Millimeter/submillimeter Array (ALMA) observatory and the subsequent calibration and imaging we performed. The image products are combinations of multiple 12m array configurations created from a selection of the observed bandwidth using multi-term, multi-frequency synthesis imaging and deconvolution. The data products are self-calibrated and exhibit substantial noise improvements over the images produced from the delivered data. We compare different choices of continuum selection, calibration parameters, and image weighting parameters, demonstrating the utility and necessity of our additional processing work. Two variants of continuum selection are used and will be distributed: the "best-sensitivity" data, which include the full bandwidth, including bright emission lines that contaminate the continuum, and "cleanest", which select portions of the spectrum that are unaffected by line emission. We present a preliminary analysis of the spectral indices of the continuum data, showing that the ALMA products are able to clearly distinguish free-free emission from dust emission, and that in some cases we are able to identify optically thick emission sources. The data products are made public with this release.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.068 | 0.056 |
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".