Kinematics of foraging dives and lunge-feeding in fin whales
Bibliographic record
Abstract
Fin whales are among the largest predators on earth, yet little is known about their foraging behavior at depth. These whales obtain their prey by lunge-feeding, an extraordinary biomechanical event where large amounts of water and prey are engulfed and filtered. This process entails a high energetic cost that effectively decreases dive duration and increases post-dive recovery time. To examine the body mechanics of fin whales during foraging dives we attached high-resolution digital tags, equipped with a hydrophone, a depth gauge and a dual-axis accelerometer, to the backs of surfacing fin whales in the Southern California Bight. Body pitch and roll were estimated by changes in static gravitational acceleration detected by orthogonal axes of the accelerometer, while higher frequency, smaller amplitude oscillations in the accelerometer signals were interpreted as bouts of active fluking. Instantaneous velocity of the whale was determined from the magnitude of turbulent flow noise measured by the hydrophone and confirmed by kinematic analysis. Fin whales employed gliding gaits during descent, executed a series of lunges at depth and ascended to the surface by steady fluking. Our examination of body kinematics at depth reveals variable lunge-feeding behavior in the context of distinct kinematic modes, which exhibit temporal coordination of rotational torques with translational accelerations. Maximum swimming speeds during lunges match previous estimates of the flow-induced pressure needed to completely expand the buccal cavity during feeding.
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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) | 0.001 | 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".