Localizing North Atlantic Right Whales using a deformable sonobuoy grid
Bibliographic record
Abstract
In 2018, a large-scale data collection effort was conducted in the Gulf of St. Lawrence, a known feeding ground for North Atlantic Right Whales (NARW), over two days. On each day, visual surveys were conducted, 32 sonobuoys were deployed to gather directional acoustic time series, and a Slocum glider operated in the area to collect oceanographic data. Following the collection phase, the acoustic data were manually annotated with a focus on NARW vocalizations. This project uses the multi-modal dataset to test and compare the results of three localization algorithms for NARW calls. The first method of localization used the directionality of the calls, where probability density maps were created by overlapping the bearing statistics across each of the relevant sonobuoys, commonly referred to as cross-fixing. To improve the bearing distributions, the signals were isolated with thresholding after using a conditional whitener and power-law statistic on its short-time Fourier transform. In the next approach, localization was performed using a spherical interpolation method to initialize a maximum likelihood time-difference-of-arrival algorithm. Finally, matched-field processing was used to model replica fields at potential source locations and correlate the replicas with the received pressure on the hydrophones.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".