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Potential Detection and Deterrence Ranges by Harbor Seals of Underwater Acoustic Harassment Devices (AHD) in the Bay of Fundy, Canada

2002· article· en· W2017339170 on OpenAlexaffabout
John M. Terhune, C. L. Hoover, Silke Jacobs

Bibliographic record

VenueJournal of the World Aquaculture Society · 2002
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine animal studies overview
Canadian institutionsUniversity of New Brunswick
Fundersnot available
KeywordsPhocaHarbor sealBaySound (geography)UnderwaterAcousticsBioacousticsSound pressureFisheryPredationPulse (music)Environmental scienceAudiologyOceanographyBiologyGeologyPhysicsEngineeringMedicineEcologyVoltageElectrical engineering

Abstract

fetched live from OpenAlex

Abstract.— Underwater acoustic harassment devices (AHDs) are designed to deter seals from attacking fin‐fish aquaculture cages. They may also be unintentionally disturbing non‐target marine mammals. As the distance from the sound source increases, the rate at which the sound pressure level (SPL) drops per unit distance decreases. This prohibits presenting loud sounds near the cages without also creating sounds that can be detected kilometers away. In situ measurements of the sound fields of two AHDs in the Bay of Fundy, Canada, were made. The distances at which a loud AHD with short duration pulses (1.8 msec, 195 dB re 1 μPa at 1 m) and a quieter AHD with longer pulse durations (>50 msec, 166 dB re 1 μPa at 1 m) would be 80 dB or more above the detection threshold of a harbor seal Phoca vitulina were 10 and 3.5 m, respectively. On a quiet day with no nearby vessel traffic, these AHDs would be clearly detectable by harbor seals at ranges up to 2.9 and 1.3 km, respectively. By increasing the pulse length of the louder AHD to >50 msec, the sound would be more than 80 dB above the seal's detection threshold at 100 m but the sound would also be clearly detectable up to 7.2 km. It is technically possible to produce AHDs that are louder. Using very high amplitude sounds to protect finfish aquaculture cages from seal predation without encountering prohibitively large capital and operating costs may not be feasible.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.607
Threshold uncertainty score0.985

Codex and Gemma teacher scores by category

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

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.

Opus teacher head0.009
GPT teacher head0.207
Teacher spread0.198 · 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 teacher head, 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

Citations15
Published2002
Admission routes2
Has abstractyes

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