Classifying and combining herd surface activities and individual dive profiles to identify summer behaviours of beluga (<i>Delphinapterus</i> <i>leucas</i>) from the St. Lawrence Estuary, Canada
Bibliographic record
Abstract
Studies of the behaviour of diving animals usually focus on either individual dives or surface group activities, but these complementary observations are seldom combined in the same study. We here study the summer (June–October) behaviour of St. Lawrence Estuary belugas (Delphinapterus leucas (Pallas, 1776)) by combining fine-scale individual diving data from 27 time–depth–speed recorder deployments (conducted in 2002–2005) with surface activity data from 1413 focal herd follows (conducted in 1991–2012). We classified 6312 dives into seven dive types based on shape and swim speed. Dives were then combined into five bout types, including three pelagic, one benthic, and one near-surface. We classified surface activities of herds into six clusters, differentiated primarily by their associated movement patterns (milling or directional) and additionally by herd structure and dispersion and occurrence of acrobatic surface events. Finally, we used herd focal follows conducted while tracking an individual beluga to relate dive and bout types to surface activities. Results indicate that milling at the surface was more frequently related to benthic dives, potentially, associated with behaviours such as benthic foraging, resting, socializing, and care of young. Directional surface movements were more frequently associated with pelagic dives likely used during pelagic foraging, exploration, and travelling.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 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".