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
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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".