Humpback whale call repertoire on a northeastern Newfoundland foraging ground
Bibliographic record
Abstract
Abstract Humpback whales (Megaptera novaeangliae) are a highly vocal baleen whale species with a diverse acoustic repertoire. “Song” has been well studied, while discrete “calls” have been described in a limited number of regions. We aimed to quantitatively describe calls from coastal Newfoundland, Canada, where foraging humpback whales aggregate during the summer. Recordings were made in July–August 2015 and 2016. Extracted calls were assigned to call types using aural/visual (AV) characteristics, and then agreement between quantitative acoustic parameters and qualitative call assignments was assessed using a supervised random forest (RF) analysis. The RF classified calls well (96% agreement) into three broad classes (high frequency (HF), low frequency (LF), pulsed (P)), but agreement for call types within classes was lower (LF: 63%; P: 85%; HF: 81%). We found support for a repertoire of 13 call types based on either high (≥70%) RF agreement (9 call types) or high (≥70%) AV agreement between two observers (4 call types). Five call types (swops, droplets, teepees, growls and whups) were qualitatively similar to call types from other regions. We propose that the variable classification agreement is reflective of the graded nature of humpback whale calls and present a gradation model to demonstrate the suggested continuum.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".