Predicting mesoscale movement of sperm whale units in the Caribbean based on social dynamics
Bibliographic record
Abstract
Sperm whales (Physeter macrocephalus) navigate complex oceanic environments and social structures. In the waters off Dominica, female and juvenile whales form long-lasting social units and vocal clans, distinguished by unique click dialects known as codas. While prey availability is often seen as a driver of whale movements, we highlight the role of sociality in shaping spatial behavior. Using 20 years of photo-identification data, we examined the sequential presence of social units for predictable patterns linked to social structure. Applying long short-term memory (LSTM) neural networks to sequences of one to five days across 16 states including 14 units, mature males and unknown units, we achieved prediction accuracies over 60%, far exceeding random chance (0.00001526). We then compared unit-to-unit transition probabilities to their social association matrix using a Hemelrijk test, revealing strong alignment between movement and social bonds for some of the units. To support long-term monitoring, we developed an acoustic classification method based on inter-pulse intervals (IPIs) in echolocation clicks, serving as acoustic fingerprints linked to body size. Kernel Density Estimation (KDE) classified units with 78.26% accuracy. Our findings provide quantitative evidence that sperm whale movements are socially coordinated and predictable, offering new insights into the spatial and social dynamics of sperm whale societies and highlighting the role of social affiliation in shaping large-scale movement patterns.
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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".