Characteristics and spatiotemporal variation of sei whale (<i>Balaenoptera borealis</i>) downsweeps recorded in Atlantic Canada
Bibliographic record
Abstract
The call characteristics and vocal behaviour of sei whales (Balaenoptera borealis) off eastern Canada, including potential spatiotemporal variation, is poorly understood. Such information can improve the performance of automated detector-classifiers, enhancing the accuracy and efficiency of identifying sei whales in large acoustic datasets. Ultimately, these data can be used to understand the occurrence, distribution, and population structure of sei whales in Atlantic Canada. We measured sei whale downsweep characteristics recorded from six locations off Nova Scotia (NS) and Newfoundland and Labrador (NFLD), Canada over a two-year period (2015-2017), and examined variation between call subtypes (singlets, doublets, triplets+), and seasons (Spring, Summer, Fall, Winter). We found that downsweeps had a mean duration of 1.58 s, sweeping from 75.66 to 34.22 Hz, with a peak frequency of 43.89 Hz and an intra-call interval of 2.22 s. Most call characteristics did not vary between location, subtype, or season; however, significantly longer downsweeps occurred off NS, within doublet calls, and in fall months. We also found that NFLD had a higher proportion of doublets (70%) than NS (52%). This variation may be evidence of acoustically diverging sei whale populations, as well as useful for improving detector-classifiers of sei whales in the region.
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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 | 0.001 |
| 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) | 0.001 | 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".