Discovering and securing TNOs: the CFHTLS Ecliptic survey
Bibliographic record
Abstract
We have developed an international collaboration aimed at discovering and long-term tracking of a large Trans-Neptunian Object (TNO) sample. The scientific rationale behind this extended observational effort is to understand the dynamical structure of the outer Solar System. This structure provides a unique tracer of planetary accretion processes and constrains models of formation and early evolution of our outer Solar System. Our observational program is designed to first discover a large sample of TNOs in well characterized surveys and then track them in a manner which will avoid what we call ‘follow-up bias’. We first briefly describe the current status of our current observational knowledge of the Kuiper Belt. Next we show how following-up almost all objects discovered in a survey has changed our view of the dynamical structure of the Kuiper Belt. Thanks to our work, previously empty places have been filled in, the relative importance of the then known dynamical population have been largely modified, and a new, potentially very large, population have been discovered. Discoveries presented in this paper were done at CFHT, while recoveries were performed on multiple telescopes, including in particular the ESO telescopes and the MPIA telescopes in Calar Alto (Spain). Finally, we briefly describe the ecliptic component of the CFHT Legacy Survey for which Kuiper Belt science is the main driver. Our experience with discovery and follow-up observations has led us to design an efficient time-sequence of observations for this survey.
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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 | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".