Underwater acoustic localization of sperm whales with a pair of hydrophones
Bibliographic record
Abstract
An iterative Bayesian approach using a pair of hydrophones was applied for the localization of vocalizing (click producing) sperm whales off the southern coast of Crete in the Eastern Mediterranean Sea in June 2015. The localization method relies on the time difference between direct and surface reflected arrivals at each hydrophone as well as between the direct arrivals at the two hydrophones, and on the knowledge of the hydrophones depths, whereas it accounts for refraction due to a depth-dependent sound-speed profile. The method provides range and depth estimates as well as estimates for the localization uncertainty reflecting measurement errors and environmental uncertainties. Further, by assuming a simple model for the array geometry bearing estimates were obtained. The inherent left-right ambiguity in bearing estimation can be resolved by changing the array orientation through maneuvering. The localization results were verified through encounters with the sperm whales when they ascended to the surface. [Work supported by EU-Greece through the Aristeia-II program and the NSRF 2014-2020/PERAN project.]
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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) | 0.001 | 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".