A Multi-Dimensional Examination of Foraging Habitat Use by Gray Whales Using Long Time-Series and Acoustics Data
Bibliographic record
Abstract
) show high site fidelity to feeding and breeding areas. The whales' annual cycle could be learned or be driven by factors such as prey abundance or ocean conditions. Long-term line transect and photo-identification data were analyzed to consider intra- and inter- annual patterns of habitat use and the underlying drivers for foraging areas in Clayoquot Sound, British Columbia. Time series, autocorrelation and weighted means analysis were used on the 20-years of data (1997-2016). A generalized additive model showed that whale use of the area was most strongly influenced by the maximum number of whales, and the date of its occurrence, recorded the previous year. This maximum, when it occurred in the summer, impacted the whale numbers for up to four subsequent years. The annual average number of whales per transect also influenced the proportion of whales known to return in multiple years to forage. Many of these returning whales first used the site to wean and returned in subsequent years to feed. The transect data was also used to contextualize passive acoustic recordings, comparing call type and rate for periods when the whale number, location and social context was known. Calling patterns appeared to be socially derived, with shorter-range knock calls dominant when whales were closer, and lower-frequency moans when foraging occurred when whales were more distant from each other. This suggests that prey-finding and site use may also be influenced by conspecifics.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.003 |
| 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 teacher head, 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".