Marine mammal phonations of Barkley Canyon: A publicly available annotated data set
Bibliographic record
Abstract
Marine mammal phonations were manually annotated in a subset of hydrophone data collected by Ocean Networks Canada from May 2013 to January 2015 to produce a data set that could be used for algorithm development and marine mammal research. Data were collected near Barkley Canyon, a biologically productive submarine canyon approximately 60 km southwest of Vancouver Island that draws aggregations of euphausiids, hake, herring, and larger animals. The data set contains 10 905 annotated phonations from fin whales, blue whales, humpback whales, sperm whales, orcas, Pacific white-sided dolphins, Risso’s dolphins, and other delphinids. All three regional orca ecotypes are represented within the data set. Humpback whale vocalizations were found in nearly ½ of all files analyzed, and fin whales were conclusively identified in approximately ¼ of files though may be present in up to ½ of files. Blue whale phonations were uncommon and only recorded in the fall and early winter. While sperm whales, Pacific white-sided dolphins, and Risso’s dolphins were noted in only 3%–7% of files, they visited the site most days. Orcas were rare visitors to the area. This data set will be made publicly available for further use.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".