The opportunity of young nearby associations with the advent of the Gaia mission
Bibliographic record
Abstract
This white paper proposes leveraging high-quality Gaia data available to the worldwide scientific community and complement it with support from Canadian-related facilities to place Canada as a leader in the fields of stellar associations and exoplanet science, and to train Canadian highly qualified personnel through graduate and post-graduate research grants. Gaia has sparked a new era in the study of stellar kinematics by measuring precise distances and proper motions for 1.3 billion stars. These data have already generated more than 1700 scientific papers and are guaranteed to remain the source of many more papers for the upcoming decades. More than 900 new age-calibrated young low-mass stars have already been discovered as a direct consequence of the second Gaia data release. Some of these may already be host stars to known exoplanet systems or may become so with the progress of the TESS mission that is expected to discover 10,000 nearby transiting exoplanets in the upcoming decade. This places Canada in a strategic position to leverage Gaia data because it has access to several high-resolution spectrometers on 1-4 m class telescopes (e.g. The ESPaDOnS, SPIRou and NIRPS), that would allow to quickly characterize this large number of low-mass stars and their exoplanet systems. This white paper describes the opportunity in such scientific projects that could place Canada as a leader in the fields of stellar associations and exoplanets.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".