Automatic detection and classification of beluga whale calls in the St. Lawrence estuary
Bibliographic record
Abstract
The endangered beluga whale (Delphinapterus leucas) of the St. Lawrence Estuary (SLEB) faces threats from a variety of anthropogenic factors. Since belugas are a highly social and vocal species, passive acoustic monitoring has the potential to deliver, in a non-invasive and continuous way, real-time information on SLEB spatiotemporal habitat use, which is crucial for their monitoring and conservation. In this study, we introduce an automatic pipeline to analyze continuous passive acoustic data and provide standard and accurate estimations of SLEB acoustic presence and vocal activity. An object detector extracted vocalizations of beluga whales from an acoustic recording of beluga vocal activity. Then, two deep learning classifiers discriminated between high-frequency call types (40-120 kHz) and the presence of low-frequency components (0-20 kHz), respectively. Different algorithms were tested for each step and their main combinations were compared in time and performance. We focused our work on a high residency area, Baie Sainte-Marguerite (BSM), used for socialization and feeding by SLEB. Overall, this project showed that accurate continuous analysis of SLEB vocal activity at BSM could provide valuable information to estimate habitat use, link beluga behavior and acoustic activity within and between herds, and quantify beluga presence and abundance.
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) | 0.000 | 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".