Effectiveness of acoustic localization in documenting spatial and temporal patterns in autumn migration of bowhead whales in the Alaskan Beaufort sea
Bibliographic record
Abstract
During September 2001, 2002 and 2003, westbound bowhead whales were localized acoustically using directional autonomous seafloor acoustic recorders (DASARs). Each year, ∼10 500–45 600 calls were detected over 23–36 days by an array of 11 DASARs deployed 6.5–22 km northeast of the oil-production island Northstar. Locations of origin for the 29% (2001) and 75% (2002) of the calls detected by two or more DASARs were determined by triangulation. Peak call detection rates occurred near 20 September in 2002 and 2003, but early in the month in 2001. In 2003, peak call detection rates exceeded 550 calls/hour. Upsweeps, downsweeps and constant-frequency calls made up 66%–68% of the calls each year. The migration corridor was similar in 2001 and 2002, but closer to shore by ∼10 km in 2003. Comparisons with yearly aerial censuses by the Minerals Management Service showed that the two methods both documented migration timing and the offshore distance of the migration corridor. Aerial surveys covered a larger area and continued after onset of freeze-up. The acoustic method revealed more temporal and spatial details because it operated continuously over long periods independent of weather and darkness, and provided far more detections of whales. [Work supported by BP.]
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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".