PolarPod-Perseverance in Arctic listening with AI to the Old World Symphonia
Bibliographic record
Abstract
Cetacean inter-species communication remains a mystery. In some fjords of the Norwegian Arctic the concentration of their prey increases due to climate change, then also the megafauna concentration, resulting in accelerating competition and species interactions. We then aim to analyze this unknow cocktail party combining simultaneously several sources : (Orca orcinus * Megap. nov. * Baleno. physalus * Physeter macro. * Landscape).This is an incredible opportunity to record for the first time these pristine soundscapes during the short season of these whale runs. It has been related in a historic report of the end of the XIXth century that such whale groupement was frequent. But since the whale industry, only Oo were present. It is only after 10 years that gradually the other species investigated the area again, with most recently the Pm (nov. 2023).Then the Center of Artificial Intelligence in Natural Acoustics (CIAN) [2] deployed its mobile acoustic laboratory on the PolarPod-Perseverance vessel. With its length of 42m, this exploration sail propulsion vessel is the future for CO2 balance, as well as for acoustic studies in dynamic conditions. Versatility and oceanographic skills of Perseverance are perfectly suited for this type of acoustic mission.We deployed a small array, Manta-1, based on our custom hydrophones and AI embedded electronic sound card. Manta-1 was used for transects by sail propulsion having a neutral impact on acoustics. We also deployed a fixed pentaphonic array and a long array. It resulted in a complex and novel corpus of whale cocktail parties that has been processed by AI listening algorithms of CIAN [1]. We then model the nictemeral acoustic cycles of each species and we test the 6x2 hypotheses of inter-species positive interactions (foraging collaboration), and negative interactions (competition).[1] ADAPREDAT, R Report, MITI CNRS, Glotin et al, (2024) https://sabiod.lis-lab.fr/pub/ADAPREDAT/AAPSanteEnvironnement2022.2_Rapportfinal_GLOTIN_FJORD3D_202403.pdf[2] https://cian.lis-lab.fr/
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".