Expanding the North Atlantic right whale call library used in acoustic monitoring
Bibliographic record
Abstract
Passive acoustic monitoring (PAM) has become an essential tool in conservation management for the critically endangered North Atlantic right whale (NARW). While PAM studies focused on detection of the commonly produced “upcall” call type have been instrumental in describing NARW presence across a variety of spatiotemporal scales, the ubiquitous production of upcalls makes it more difficult to determine the behavioral context of detected NARWs. Here we have examined an extensive 26-year dataset of NARW acoustic recordings ranging from the Canadian Bay of Fundy to the Southeastern United States to identify other common NARW call types and their associated behavioral context. Calls were grouped into discrete call types based on a contour feature analysis, then examined for context via visual observations and/or movement data from biologging tags. We identified two stereotyped mid-frequency call types, “downcalls” and “constant calls,” produced across regions during social surface active group behaviors. We propose that these call types could be used to identify periods where groups of NARWs are at or just below the surface. The implementation and analysis of these call types in acoustic monitoring would enhance the quality of data collected, allowing for identification of potential right whale aggregations through PAM studies.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".