Structure and Dynamics of Minke Whale Surfacing Patterns in the Gulf of St. Lawrence, Canada
Bibliographic record
Abstract
Animal behavioral patterns can help us understand physiological and ecological constraints on animals and its influence on fitness. The surfacing patterns of aquatic air-breathing mammals constitute a behavioral pattern that has evolved as a trade-off between the need to replenish oxygen stores at the surface and the need to conduct other activities underwater. This study aims to better understand the surfacing pattern of a marine top predator, the minke whale (Balaenoptera acutorostrata), by investigating how their dive duration and surfacing pattern changes across their activity range. Activities were classified into resting, traveling, surface feeding and foraging at depth. For each activity, we classified dives into short and long dives and then estimated the temporal dependence between dive types. We found that minke whales modified their surfacing pattern in an activity-specific manner, both by changing the expression of their dives (i.e. density distribution) and the temporal dependence (transition probability) between dive types. As the depth of the prey layer increased between activities, the surfacing pattern of foraging whales became increasingly structured, going from a pattern dominated by long dives, when feeding at the surface, to a pattern where isolated long dives were followed by an increasing number of breaths (i.e. short dives), when the whale was foraging at depth. A similar shift in surfacing pattern occurred when prey handling time (inferred from surface corralling maneuvers) increased for surface feeding whales. The surfacing pattern also differed between feeding and non-feeding whales. Resting whales did not structure their surfacing pattern, while traveling whales did, possibly as a way to minimize cost of transport. Our results also suggest that minke whales might balance their oxygen level over multiple, rather than single, dive cycles.
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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 | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".