SeGaMas : Serious Game for Marine Mammals Survey
Bibliographic record
Abstract
Studying cetaceans is a complex task, due to the inaccessibility of the marine environment and low visibility. Passive acoustics is a very promising non-invasive solution. Nowadays, detection and classification of recorded species are carried out automatically and with good accuracy, in particular using AI methods. However, using such methods to locate animals is still imprecise, due to the lack of ground truth data on their actual movements.To overcome this, we are working on the creation of a complete model of acoustic scenes, called SeGaMas (Serious Game for Marine (mammals) Survey). This model includes the generation of realistic cetacean trajectories, inspired by [1] and [2] , the regular emission of a biophonic signal, and a ray-tracing model to reconstruct the signal arriving at the sensor. As inputs, the model receives bathymetric, oceanographic and ambient noise data. As output, it provides animal trajectories, the received signal and the paths taken by this signal.These simulated data are then used to create a “click sequences to trajectories” AI model, capable of reconstructing the trajectory of a sound source from a simple recording. They will also be useful in the study of cetacean perceptions, and to better design and position our sensors.[1] Parrott et al (2010). 3MTSim: An agent-based model of marine mammals and maritime traffic to assist management of human activities in the Saint Lawrence Estuary, Canada.[2] Chouchane et al (2012), EvoCOP 2012: Splitting method for spatio-temporal sensors deployment in underwater systems
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".