Planetary Astronomy-Understanding the Origin of the Solar System
Bibliographic record
Abstract
There is a vibrant and effective planetary science community in Canada. We do research in the areas of meteoritics, asteroid and trans-Neptunian object orbits and compositions, and space weather, and are involved in space probe missions to study planetary surfaces and interiors. For Canadian planetary scientists to deliver the highest scientific impact possible, we have several recommendations. Our top recommendation is to join LSST and gain access to the full data releases by hosting a data centre, which could be done by adding to the CADC, which is already highly involved in hosting planetary data and supporting computational modelling for orbital studies. We also support MSE, which can provide spectroscopy and thus compositional information for thousands of small bodies. We support a Canadian-led microsatellite, POEP, which will provide small body sizes by measuring occultations. We support the idea of piggybacking space weather instruments on other astronomical space probes to provide data for the space weather community. Many Canadian planetary scientists are involved in space probe missions, but through haphazard and temporary arrangements like co-appointments at US institutions, so we would like the community to support Canadian researchers to participate in these large, international missions.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.004 |
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".