Simultaneous acoustic tag and seafloor acoustic recorder detection of right whale calls in the Bay of Fundy.
Bibliographic record
Abstract
Passive acoustic monitoring is playing a growing role in marine mammal detection. Determining the range of detection for calls of a particular species in a particular location is important to assess the regional coverage provided by individual recording units. This study describes the comparison of right whale calls recorded by digital acoustic recording tags (Dtags) attached with suction cups to North Atlantic right whales and the detection of the same calls using a dispersed seafloor array of autonomous recorders. The seafloor array consisted of 5 units, spaced 6–10 km apart, continuously recording from July 29– August 17, 2005. Dtags were attached to a total of 14 individual right whales during this time period and 7 of these individuals produced a total of 88 tonal calls during tag attachment. The tag and related tracking of the whale provided information on call type, and the timing, depth, and approximate location of the whale producing the call. Tagged whale calls were audible on the seafloor array, and whale-recorder distances provided estimates of the acoustic detection range for right whales in the Bay of Fundy, Canada.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".