Using ocean gliders to characterize baleen whale habitat in the Northwest Atlantic
Bibliographic record
Abstract
Characterizing baleen whale habitat is challenging because of the difficulty in obtaining sufficient spatially and temporally concurrent in situ observations of whales and oceanographic conditions. We collected a multi-year series of concurrent whale detections and high-resolution oceanographic measurements from Slocum ocean gliders to evaluate baleen whale habitat associations. The study area was Roseway Basin, a relatively small (30 × 60 km), shallow (<180 m) basin located ~40 km seaward of SW Nova Scotia, Canada. Data were collected from 13 fall (August-November) glider surveys of the basin over an 8 yr period (2014-2021). Gliders collected profiles of salinity and temperature as well as audio to detect and classify whale sounds. Acoustic analysis revealed spatial, diel, and within-season patterns in whale detections. Whale occurrence and a suite of oceanographic variables were computed in 20 km grid cells in each month and year of the study (n = 267). Descriptive and statistical (logistic regression) analyses were used to explore associations between the occurrence of each species and depth, topographic relief, water column stratification, current speed, and bottom mixed layer thickness and density. Results suggested strong, positive associations for fin, sei, and right whale occurrence and depth. They also showed that right whale occurrence in August-September was associated with a well-stratified water column overlying a thick, dense bottom mixed layer, consistent with conditions known to have a role in aggregating their copepod prey. Although exploratory, our results demonstrate the utility of profiling gliders for making inferences about baleen whale habitats.
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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 | 0.001 |
| 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".