Integrating acoustic and genetic methods to understand cetacean presence and distribution
Bibliographic record
Abstract
The "Sphyrna Odyssey 2019-2020" mission combined passive acoustic monitoring and environmental DNA (eDNA) to monitor marine mammals in the Mediterranean, focusing on areas impacted by heavy marine traffic. Sphyrna autonomous vessels, equipped with hydrophones, provided real-time detection of vocalizing species [1], while eDNA sampling from surface waters captured genetic traces over larger spatial areas, including busy shipping lanes [4]. Acoustic monitoring provided real-time data on vocalizing species, while eDNA detected species even in the absence of vocalizations, though factors like ocean currents and DNA degradation influenced results [3]. Both methods identified eight cetacean species, revealing higher densities along continental slopes, submarine canyons, and areas with significant shipping traffic. This study highlights the value of integrating acoustic and genetic methods to understand cetacean presence and distribution, crucial for conservation in high-impact regions [2].[1] Glotin, H., Spong, P., Symonds, H., Roger, V., Balestriero, R., Ferrari, M., ... & Dakin, T. (2018) Deep learning for ethoacoustical mapping: application to a single Cachalot long term recording on joint observatories in Vancouver Island. The Journal of the Acoustical Society of America, 144(3), 1776-1777.[2] Glotin H., Thellier N., Best P., Poupard M., Ferrari M., Vieira S., Giés V., ... Sarano F., Benveniste J., Gaillard S., de Varenne F. (2020) Sphyrna-Odyssey 2019-20, Découvertes Etho-acoustiques de Chasses Collaboratives de Cachalots en Abysse & Impacts en Mer du COVID19, http://sabiod.org/pub/SO1.pdf, 197p, Univ. de Toulon, CNRS[3] Collins, R. A., Wangensteen, O. S., O’Gorman, E. J., Mariani, S., Sims, D. W., & Genner, M. J. (2018). Persistence of environmental DNA in marine systems. Communications Biology, 1(1), 185.[4] Ficetola, G. F., Miaud, C., Pompanon, F., & Taberlet, P. (2008). Species detection using environmental DNA from water samples. Biology letters, 4(4), 423-425.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".