Utilisation de la surveillance acoustique passive à partir de planeurs pour la détection en temps quasi réel des baleines noires de l'Atlantique Nord (Eubalaena glacialis) pour supporter la gestion des zones de navigation dynamiques du chenal Laurentien et de la zone de ralentissement volontaire du détroit de Cabot (2021-2022).
Bibliographic record
Abstract
This project used right whale detections from gliders to trigger dynamic vessel management in the traffic separation scheme within Dynamic Shipping Zone C (DSZ C) and the Cabot Strait Voluntary Slow Zone. Two profiling gliders equipped with hydrophone systems were deployed for 116 and 107-day missions, respectively, from July 6th – November 9th, 2021. The gliders reported daily detections of four baleen whale species that were validated by a trained human analyst and then sent to Transport Canada. The glider detected right whales in DSZ C on fourteen survey days, and the Cabot Strait on two survey days. In response to the glider detections, a speed limit was imposed in DSZ C for 72 days, or 62.1% of the survey period. The false positive rates for right whale detections at the daily scale were 7% and 0%, and the false negative rates for this species at the daily scale were 5% and 1%, respectively.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".