Recycling data: An annotated marine acoustic data set that is publicly available for use in classifier development and marine mammal research
Bibliographic record
Abstract
Barkley Canyon is a productive submarine canyon approximately 60 km southwest of Vancouver Island, Canada. The canyon's nutrient flow is affected by multiple regional currents, and draws aggregations of euphausiids, hake, herring, and various marine mammal species. A subset of the acoustic data collected from the 2013–2015 hydrophone deployment on the Barkley Canyon Upper Slope platform of Ocean Networks Canada's North-East Pacific Time-series Undersea Networked Experiments (NEPTUNE) observatory was manually annotated for marine mammal presence to investigate marine mammal habitat use and in support of development of a random forest classifier. This dataset, which is being made publicly available for further use, includes strong-label annotations of phonations from blue whales, fin whales, humpback whales, sperm whales, orcas, Pacific white-sided dolphins, Risso's dolphins, and other delphinids that could not be identified to species. All regional orca communities are represented within the dataset, and phonations are labelled to ecotype and pod level when possible. This dataset could be further used in a number of ways, including classifier development, investigations into habitat use and seasonality, or combining the acoustic data with data collected from co-located oceanographic instruments to investigate links between marine mammal presence and oceanographic conditions.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.018 | 0.034 |
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".