Investigation of the context of humpback whale non-song calls in the North Atlantic
Bibliographic record
Abstract
Humpback whales display behavioural plasticity in their foraging behaviours and non-song calling behaviours. Prey type appears to be associated with some of the variation in foraging behaviours and, thus, may also be related to variation in use of non-song calls related to foraging. Our goal was to compare non-song calls from two regions and identify any call types that occur in only one of the regions. We used recordings from stationary hydrophones within two foraging grounds: from the northeast Newfoundland coast, where capelin is the main prey; and the Gulf of Maine, where herring and sand lance dominate the diet. These areas are sympatric and foraging groups are thought to be from the same breeding aggregation, suggesting that call type differences would not have a genetic-basis. Most of the call types were the same across both areas, as has been found across many foraging grounds, but a small number of call types and call sequences were heard in only one of the two regions. We hope to use multi-sensor tag data and video recordings to help identify the contexts of these calls to further determine whether non-song call variation can be linked to prey type.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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".