FindPorpoises: Deep learning for detection of harbor porpoise echolocation clicks
Bibliographic record
Abstract
Tom Norris expressed conservation concern for the vaquita, a close relative of harbor porpoises; here we report on a harbor porpoise (Phocoena phocoena) conservation effort. Tidal energy devices are installed in high-flow estuaries that are also prime harbor porpoise habitat. To study tidal energy device impacts on porpoises, sound was recorded in Minas Passage, Bay of Fundy, Canada. Analysis aimed to distinguish harbor porpoises from noise sources. “Click candidate” sounds were detected using the ratio between the harbor porpoise frequency band and lower guard band, then reviewed by humans to label which were correct. Because more “correct” instances were needed, data were augmented by mixing porpoise clicks with known noise, producing 20,000 “click present” labeled instances. Additionally, 20 000 “non-click” instances were extracted from noise recordings. Labeled instances were made into a 0.5-s equalized spectrograms for training deep-learning networks. Of the network architectures tried, the best was a convolutional neural network with pooling and fully connected layers, achieving 99.1% accuracy. User-friendly “FindPorpoises” software was made to pre-process raw files, detect candidate clicks, use the trained network to sort candidate clicks into correct and incorrect instances, and plot and tabulate the results by time of day, tide cycle, and month. [Work supported by OERA.]
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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 | 0.001 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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; 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".