Acoustic detection and localization of whales in bay of fundy and St. Lawrence estuary critical habitats
Bibliographic record
Abstract
The detection and localization of marine mammals using passive acoustics is explored for two critical habitats in Eastern Canada.Two-dimensional hyperbolic localization is performed on time differences of arrivals o f specific calls on grids of coarsely spaced autonomous recorders and on a shore-linked coastal array o f closely spaced hydrophones.Delays are computed from cross-correlation and spectrogram cross coincidence on signals enhanced with high-frequency emphasis and noise spectral suppression techniques.The outcomes and relative performance of the two delay estimation methods are compared.The difficulties encountered under the particular conditions o f these two environments are discussed for the point o f view o f automated localisation for monitoring whales. RÉSUMÉLa détection et la localisation de mammifères marins à l'aide de l'acoustique passive est explorée pour deux habitats critiques dans l'est du Canada.La technique de localisation par hyperboles en deux dimensions est utilisée à partir des différences de temps d'arrivée à des réseaux de systèmes d'enregistrements autonomes largement espacés, ainsi qu'à un réseau serré d'hydrophones reliés à la côte.Les délais d'arrivée sont calculés par inter-corrélation ainsi que par inter-coincidence des spectrogrammes des signaux rehaussés par des techniques de rehaussement des hautes fréquences et de soustraction spectrale du bruit.Les résultats et la performance relative des deux méthodes sont comparés.Les difficultés rencontrées dans le contexte des conditions particulières de ces deux environnements sont discutées par rapport à l'automatisation de la localisation pour le monitorage des baleines.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".