Signposts of planet formation in protoplanetary disks
Bibliographic record
Abstract
Successful exoplanet surveys in the last decade have revealed that planets are ubiquitous throughout the Milky Way, and show a large diversity in mass, location and composition. At the same time, new facilities such as the Atacama Large Millimeter/submillimeter Array (ALMA) and optical/infrared facilities including Gemini/GPI have provided us with sharper images than ever before of protoplanetary disks around young stars, the birth cradles of planets. The high spatial resolution has revealed astonishing structures in disks, such as rings, gaps, asymmetries and spiral arms, and the enormous jump in sensitivity has provided the tools for both large, statistically relevant surveys and deep, sensitive molecular line studies. These observations have revolutionized our view of planet formation, disk formation and disk evolution, bringing model simulations and observations closer to the same level of detail, with many contributions from Canadian researchers on theoretical, observational and technological sides. The new results have inevitably led to a range of new questions, which require next generation instruments such as the Next Generation Very Large Array (ngVLA) and large scale optical infrared facilities. In this white paper we will discuss the current transformation in our understanding of planet formation and the next steps and challenges in connecting theory with exoplanet demographics and protoplanetary disk observations for Canadian research.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".