Automated detection of white whale (delphinapterus leucas) vocalizations in St. Lawrence estuary and occurrence pattern
Bibliographic record
Abstract
A detailed behavioral and habit study on the vocalization of the white whale population, is presented. White whales are known for their high degree of acoustic activities and their vocalization are variable in time and frequency. An automated method using a sequence of signal processing algorithms is developed to detect sound of white whale. A threshold is applied to transform the spectrogram into a binary image on which residual noise is cleaned using two specific image filters. The frequency band of the vocalizations is relatively stable over the seven days of sampling, while the vocalization rate is variable from day to day. This method can detect all the diverse white whale calls and pulsed tones emerging in the signal after noise filtration. The vocalization rate intensity of the false detections is very low compared to that of the detected calls. The prime frequency band used by white whales in recorded data is slightly lower than the estimated frequency rate.
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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 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".