Monitoring haddock in the Stellwagen Bank National Marine Sanctuary using passive acoustics
Bibliographic record
Abstract
Haddock (Melanogrammus aeglefinus) are important commercial resources in the western North Atlantic. Their distribution ranges from Newfoundland to North Carolina and are most abundant on Georges Bank and in the Gulf of Maine. Over the course of a decade, the Gulf of Maine warmed faster than 99% of the global ocean and these changes are likely to impact the distribution and reproduction of haddock. It is therefore increasingly important to closely monitor this species and ensure a sustainable fishery. The objective of this study is to monitor the spatial and temporal occurrence of haddock using the sound they produce. We manually annotated over 20 000 haddock calls at five different sites and four different years and used them to train a convolutional neural network that can automatically detect haddock calls in acoustic recordings. The detector was then used to analyze several months of passive acoustic data collected in the Stellwagen Bank National Marine Sanctuary. The analysis revealed that haddock calls were mostly detected during the spawning season (January to March) but were also detected as late as August. We discuss how passive acoustics can complement existing monitoring methods and support the management of this commercially and ecologically important species.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".