Effects of the sounds from an artificial oil production island on bowhead whale calling behavior
Bibliographic record
Abstract
The westward migration of bowhead whales (Balaena mysticetus) was studied during autumn 2001–2004 to examine the effects of sounds from an artificial oil production island (Northstar) on whale calling behavior. Whale calls were recorded by an array of directional autonomous seafloor acoustic recorders (DASARs) located 6.4–21.5 km northeast of Northstar in the Beaufort Sea. Simultaneously, a continuous record of sounds produced by Northstar was obtained ∼450 m north of the island. More than 130,000 bowhead calls were detected, and were classified as to their type, including five types of simple calls and one complex call category. In addition, the directional capability of DASARs allowed triangulation of an estimated whale position for a majority of the calls. The primary objectives of the study were to assess any effects of Northstar sounds and environmental-physical covariates on the duration, midfrequency, frequency range, type of call, and call detection rates. The analyses showed that an increase in transient sounds from Northstar (i.e., boats) resulted in significantly shorter calls. In addition, call detection rates were significantly higher for whales upstream of the array, and the use of complex calls increased significantly as the whales swam westward past Northstar. [Study funded by BP Exploration, Alaska.]
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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 | 0.000 | 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".