Western and central North Atlantic fin whale (<i>Balaenoptera physalus</i>) stock structure assessed using geographic song variations
Bibliographic record
Abstract
Variations in calls or songs between areas are increasingly acknowledged as a way to assess stock structure. We present the results of an analysis of 163 fin whale (FW) songs recorded in seven areas of the North Atlantic (NA): Irminger Sea, Davis Strait, Grand Banks of Newfoundland, Southern Newfoundland, Gulf of St. Lawrence, eastern Scotian Shelf, and the waters off Delaware Bay. Song measurements included inter-note intervals (INI), notes' peak frequency and bandwidth and note type (classic, backbeat, and high-frequency) proportion. Seasonal patterns of classic-classic INI provided the highest level of differentiation between areas and revealed the existence of six acoustic stocks. Classification trees revealed that other parameters distinguished between regions over larger spatial scales, grouping some of the recording areas together. These results suggest that (1) there are four distinct stocks in the western NA; (2) the range of a presumed central NA stock includes southwestern Iceland, both sides of Greenland and appears to extend south along the Mid-Atlantic Ridge, at least in recent years; (3) two stocks are present off West Greenland. These results bring new information on potential FW stock delineations in the NA. The latter will be compared to those derived using other stock assessment metrics.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".