Quantification, characterization and attenuation of the acoustic footprint of the maritime transport in the St. Lawrence seaway - The MARS observatory
Notice bibliographique
Résumé
Marine traffic is the main contributor to ocean noise at low frequencies, contributing to an observed increase of up to 10 dB over the few decades. It is predicted to further intensify over the coming decades and expand dramatically in the Arctic following new routes made accessible by global warming and sea-ice melt. Anthropogenic noise has a demonstrated impact on marine environment, through masking of intraspecific and interspecific communication, affecting predator-prey interactions hampering settlement cues and reducing threat (including vessel) detection, resulting in increased stress levels and reduction of habitat suitability. The St. Lawrence Estuary (eastern Canada) is a major shipping corridor linking the Great Lakes to the Atlantic Ocean, and home to 13 marine mammal species including Endangered Beluga whale and blue whale and Critically Endangered North Atlantic right whale. Understanding and reducing the acoustic footprint of maritime transport is critical to improve its cohabitation with the marine fauna.The Marine Acoustic Research Station (MARS, www.projet/mars.ca/enwww.projet-mars.ca/en) is an applied research project focused on quantifying, understanding and attenuating traffic noise and its effects on marine life. An acoustic recording station was specifically designed to collect high-resolution measurements of the source levels radiated by a significant part of the commercial fleet operating in the St. Lawrence Seaway. The MARS observatory contributes to improving knowledge of the underwater noise emissions in the St. Lawrence Estuary through quantitative measurements, comprehensive analysis and modeling of ship noise. From our high-resolution source level database, we developed a source level model, tailored to the St. Lawrence fleet, to further understand, model and predict the acoustic footprint of traffic noise in the region, critical to conservation actions and traffic management.Over the first three years of operation, we collected over 2500 source level measurements. A suite of autonomous onboard vibration sensors has been developed and deployed on 15 ships for comprehensive characterization onboard vibrations. Our observations provide quantitative information about the underwater noise generated by the St. Lawrence fleet to the government, for informed decisions regarding the establishment of source level limits. We also deliver quantitative feedback to shipowners regarding the contribution of each of their vessels to underwater noise, and we help raising awareness within their teams and identifying suitable attenuation solutions. Finally, the MARS team contributes to improve ship usage, modification and design to reduce noise emissions, through characterization of the mechanical processes, onboard the ships, related to noise generation, transmission, and radiation in the ocean.The long-term operation of the MARS observatory offers a unique framework to advance scientific and technical knowledge regarding ship noise, and a robust measurement infrastructure to test and quantify the effects of noise mitigation solutions for individual vessels through successive measurements, as well as to monitor the fleet-wide effects of traffic management decisions and technology development over the years on the acoustic footprint of maritime transport.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».