MétaCan
Menu
← Back to cohort
Record W4408817447 · doi:10.5194/oos2025-1479

Integrating acoustic and genetic methods to understand cetacean presence and distribution

2025· preprint· en· W4408817447 on OpenAlexaboutno aff
Sara Vieira, Fabien de Varenne, Sandrine Gaillard, Renata S. Sousa‐Lima, Hervé Glotin

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEnvironmental Science
TopicMarine animal studies overview
Canadian institutionsnot available
Fundersnot available
KeywordsDistribution (mathematics)Evolutionary biologyComputer scienceBiologyMathematics

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0040.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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.

Opus teacher head0.032
GPT teacher head0.326
Teacher spread0.294 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

Explore more

Same topicMarine animal studies overview→French-language works237,207→