Deep Learning for Ethoacoustics of Oreas on three years pentaphonie continuous recording at Orealab revealing tide, moon and diel effects
Bibliographic record
Abstract
One of the best ways of studying animals that produce signals in underwater environments is to use passive acoustic monitoring (PAM). Acoustic monitoring is used to study marine mammals in oceans, and gives us information for understanding cetacean life, such as their behaviour, movement or reproduction. Automated analysis for captured sound is almost essential because of the large quantity of data. A deep learning approach was chosen for this task, since it has proven great efficiency for answering such problematics. This study focused on the orcas (Orcinus orca) of northern Vancouver Island, Canada, in collaboration with the NGO Orcalab which developed a multi-hydrophone recording station around Hanson Island to study orcas. The acoustic station is composed of 5 hydrophones and extends over 50 km <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> of ocean. Since 2016 we are continuously streaming the hydrophone signals to our laboratory at Toulon, France, yielding nearly 50 TB of synchronous multichannel recordings. The objective for this research is to do a preliminary analysis of the collected data and demonstrate influence of environmental factors (tidal, moon phase and daily period) on the orcas' acoustic activities.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".