Pair-wise spectrogram processing used to track a sperm whale
Bibliographic record
Abstract
We developed a model-based localization method called pair-wise spectrogram (PWS) processing to track marine mammals using widely spaced hydrophone arrays [Nosal & Frazer, IEEE J. Ocean. Eng., in press]. Here we use PWS to track the sperm whale from the dataset provided for the 2005 Workshop on detection and localization of marine mammals using passive acoustics (Monaco). This dataset provides a good opportunity to validate and explore the properties of the PWS processor. We demonstrate the relationship between pair-wise processing and a time-of-arrival method for a simple case. We also show how varying the size of windows used to create spectrograms optimizes the tradeoff between processor resolution and robustness, and how these parameters can be adjusted according to grid spacing. PWS position estimates are within tens of meters of those obtained using a careful time-of-arrival method applied to individual clicks.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.002 | 0.001 |
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; both teacher heads agree on what is shown here.
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".