Analyzing potential acoustic differences between different bowhead demographics in the Beaufort Sea.
Bibliographic record
Abstract
Most bowhead whales from the Bering-Chukchi-Beaufort stock migrate westward from Arctic Canadian waters through the Alaskan Beaufort Sea each year from late August to late October. Since 2007 both aerial sighting and acoustic call data have been collected during these fall migrations, as part of a long-term monitoring effort to assess the potential impacts of North Slope oil industry activities on bowhead whales. The aerial sighting efforts recorded the position of bowhead individuals or groups, designating the animals as subadults or adults, and noted whether a calf was present. A five-site array of seven directional autonomous seafloor acoustic recorders (DASARs) each recorded the acoustic data. An automated detection algorithm was used to isolate individual bowhead whale calls and estimate their locations. Here simultaneous acoustic and visual data from 2007, 2008, and 2010 were merged to determine whether potential variations in calling behavior exist between subadults, adults, and adults with calves. Visual measurements of animal course and direction were used to place bounds on call times and locations that may be associated with that animal versus other animals sighted nearby. Potential differences in call rate and call type were statistically examined. [Work supported by the Shell Exploration and Production Company.]
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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".