Estimating minke whale relative abundance in the North Atlantic using passive acoustic sensors
Bibliographic record
Abstract
Estimates of abundance and their changes through time are key elements of marine mammal conservation and management. Absolute marine mammal abundance in a region of the open ocean is often difficult to attain. However, methods of estimating their abundance based on passive acoustic recordings are becoming increasingly employed. This study shows that passive acoustic monitoring of North Atlantic minke whales with a single hydrophone provides sufficient information to estimate relative population abundance. An automated detector was developed for minke whale pulse trains and an approach for converting its output into a relative abundance index is proposed by accounting for detectability as well as false positives and negatives. To demonstrate this technique, a 2 y dataset from the seven sites of the Atlantic Deepwater Ecosystem Observatory Network project on the U.S. east coast was analyzed. Resulting relative abundance indices confirm pulse train-calling minke whale presence in the deep waters of the outer continental shelf. The minkes are present December through April annually with the highest abundance near the site offshore of Savannah, Georgia.
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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) | 0.000 | 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".