Workshop Report: Detection and Classification in Marine Bioacoustics\n with Deep Learning
Bibliographic record
Abstract
On 21-22 November 2019, about 30 researchers gathered in Victoria, BC,\nCanada, for the workshop "Detection and Classification in Marine Bioacoustics\nwith Deep Learning" organized by MERIDIAN and hosted by Ocean Networks Canada.\nThe workshop was attended by marine biologists, data scientists, and computer\nscientists coming from both Canadian coasts and the US and representing a wide\nspectrum of research organizations including universities, government\n(Fisheries and Oceans Canada, National Oceanic and Atmospheric Administration),\nindustry (JASCO Applied Sciences, Google, Axiom Data Science), and\nnon-for-profits (Orcasound, OrcaLab). Consisting of a mix of oral\npresentations, open discussion sessions, and hands-on tutorials, the workshop\nprogram offered a rare opportunity for specialists from distinctly different\ndomains to engage in conversation about deep learning and its promising\npotential for the development of detection and classification algorithms in\nunderwater acoustics. In this workshop report, we summarize key points from the\npresentations and discussion sessions.\n
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.037 | 0.022 |
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".