Detection and localization of blue and fin whales from large-aperture autonomous hydrophone arrays: A case study from the St. Lawrence estuary
Bibliographic record
Abstract
The feasibility of using passive acoustic methods (PAM) to monitor time-space distribution of fin and blue whales in the Saguenay-St.Lawrence Marine Park was explored using large-aperture sparse hydrophone arrays.The arrays were deployed during summers 2003 to 2005 at the head of the 300-m deep Laurentian Channel.They were composed of 5 AURAL autonomous hydrophones moored at mid-water depths, near the summer sound channel.A small coastal array complemented the deployment in 2003.The apertures were from 20 to 40 km and the configurations were changed from year to year.The most frequent calls recorded were blue and fin whale signature infrasounds.Noise from transiting ships on the busy St. Lawrence Seaway often masked the calls on the nearest hydrophones.Sometimes this resulted in an insufficient number of receivers for localizing the whales using time difference of arrival (TDoA) methods.The technical characteristics of the arrays and data processing are presented, with an example of call detection and localization.Despite the difficulties inherent to this environment, PAM can be effectively implemented there, eventually for real-time operations. r é s u m éLa faisabilité d 'utiliser la technologie de monitorage acoustique passif (PAM) pour suivre la distribution spatio-temporelle des rorquals bleus et communs dans le Parc Marin Saguenay-Saint-Laurent a été explorée à l'aide de réseaux d 'hydrophones à maille lâche couvrant de grandes distances.Les réseaux ont été déployés pendant les étés 2003 à 2005 à la tête du chenal Laurentien, profond de 300 m.Ils étaient composés de 5 hydrophones autonomes AURAL mouillés à mi-profondeur, près du couloir de son estival.Un petit réseau côtier de faible ouverture complétait le déploiement en 2003.Les ouvertures des réseaux étaient de 20 à 40 km et leurs configurations étaient changées à chaque année.Les vocalisations les plus fréquentes étaient les infrasons identitaires des rorquals bleus et communs.Le bruit de navires transitant dans la Voie Maritime achalandée du Saint-Laurent masquait souvent les vocalisations sur les hydrophones les plus proches, ce qui parfois résultait en un nombre insuffisant de récepteurs pour localiser les baleines à l'aide de méthodes utilisant les différences de temps d 'arrivée (TDoA).Les caractéristiques techniques des réseaux et du traitement des données sont présentées avec un exemple de détection et de localisation.Malgré les difficultés inhérentes à cet environnement, la technologie PAM peut y être efficacement implémentée, éventuellement pour des opérations en temps réel.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".