Sperm whale acoustic ecology investigations using Jeffrey Nystuen‘s Passive Aquatic Listeners
Bibliographic record
Abstract
When Jeffrey Nystuen invented the Passive Aquatic Listener (PAL) for quantifying rainfall and wind speed using underwater sound spectra, he considered all biological sounds in the recordings as noise. Using this noise data, I built a doctoral thesis, comprised of three published papers. None of this would have happened without Jeff’s continuous encouragement and support. Here, we present results from analyzing (1) five years (2007–2012) of PAL recordings from Ocean Station PAPA (OSP) in the offshore Gulf of Alaska; and (2) 19 months of PAL data from two sites, Pylos and Athos Stations (in the Hellenic Trench and North Aegean Trough respectively), in the Greek Seas, investigating the acoustic ecology of sperm whales (Physeter macrocephalus). Results of the bioacoustic analysis revealed the year-round presence of sperm whales at OSP and the Ionian Sea, with higher detections during the warm seasons. The sperm whale time series from OSP was correlated with in situ and remotely-sensed oceanographic variables to improve our understanding of global-warming-driven changes in the pelagic ecosystem of the NE Subarctic Pacific. Results from the Hellenic Trench emphasize the risk from increased shipping noise and contribute to the conservation efforts for the small, endangered sperm whale population in an understudied 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 | 0.000 |
| 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".