The Formation of Stars - From Filaments to Cores to Protostars and Protoplanetary Disks
Bibliographic record
Abstract
Star formation involves the flow of gas and dust within molecular clouds into protostars and young stellar objects (YSOs) due to gravity. Along the way, these flows are shaped significantly by many other mechanisms, including pressure, turbulent motions, magnetic fields, stellar feedback, jets, and angular momentum. How all these mechanisms interact nonlinearly with each other on various length scales leads to the formation and evolution of substructures within clouds, including filaments, clumps, cores, disks, outflows, the protostars/YSOs themselves, and planets. In this LRP2020 white paper, we describe the observational and theoretical leadership in the star formation field that Canada’s vibrant community has demonstrated over the past decade. Drawing from this extensive background, we identify five key questions that must be addressed for further progress to be made in understanding star formation in the next decade. Addressing these questions will improve our understanding of the dynamics of the dense gas and the role of the magnetic field in star formation, the optical properties of the dust used to trace mass and magnetic fields, the sources of variability in star-forming objects on short timescales, and the physical processes that specifically promote the clustering of stars. We further highlight key facilities in which Canada should become involved to continue making progress in this field. Single-dish facilities we recommend include LSST, trans-atmospheric far-infrared telescopes like BLAST-TNG and SPICA, and ground-based telescopes like JCMT, GBT, and CCAT-p. Interferometric facilities we recommend include ALMA, ngVLA, and SKA1.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".