The next decade of optical wide field astronomy in Canada
Bibliographic record
Abstract
Internationally, wide field imaging and spectroscopy at optical/near-infrared wavelengths is having profound impacts in diverse areas of astronomy. The international portfolio of projects in this arena for the next decade has never been richer and the expectation for transformative discoveries has never been higher. We discuss numerous projects in which Canada has interests or involvement, many of which are the subject of dedicated white papers, and attempt to set the national and international context in which these projects should be viewed. Critically, we show that, without action being taken by the community and supported by the LRP, Canada is facing a lack of access to any of the emerging front-line ground-based optical wide field programs and facilities for almost the entirety of the 2020s. We provide a set of comments for consideration by the LRP and the community for how to ensure Canada instead is able to capitalize upon the riches of the next decade, to compete internationally, and to set itself in a leadership position fo the 2030s. These recommendations include obtaining new access to 2020 datasets and observatories, leveraging the enviable capacity and skills of CADC/CANFAR, and developing the community so it can excel in an increasingly crowded, and exciting, international scene.
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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 | 0.008 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.013 | 0.005 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.030 | 0.007 |
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".