Estimating propulsive efficiency of bottlenose dolphins during steady-state swimming*
Bibliographic record
Abstract
Cetaceans are phenomenal swimmers, but the marine environment makes it difficult to directly observe and quantify their dynamic swimming behavior. Biologging tags are often used to measure animal movement in the wild. But these embedded systems only measure movement kinematics where they are attached, and cannot measure the hydrodynamic forces the animals use to swim. Here, we present a framework that leverages a low-order model of dolphin swimming dynamics and kinematic data (orientation, depth, speed) collected from a biologging tag to: A) estimate the sagittal-plane body kinematics of swimming bottlenose dolphins (Tursiops truncatus); and B) estimate swimming kinetics and propulsive efficiency during steady-state swimming. Body kinematics for the segmented dolphin model were estimated from tag data using a temporal convolutional network that was trained using a synthetic data set. The estimated segment angles had errors of less than 2° from the true body joint angles. The measured and estimated kinematic data were used as inputs for the dolphin model to estimate the internal and external forces generated during swimming. The estimated kinematics and kinetics compare with published results, and the estimated propulsive efficiency were typically greater than 70% across the range of swimming speeds investigated. These results enable per stoke estimates of propulsive efficiency, and provide the foundation for an approach that can be used in the future to estimate the swimming biomechanics of dolphins in the wild.
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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 | 0.001 |
| 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 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".