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Record W2029336921 · doi:10.1080/00028487.2013.788559

Hearing Sensitivity of the Burbot

2013· article· en· W2029336921 on OpenAlexafffund
Peter A. Cott, Tom A. Johnston, John M. Gunn, Dennis M. Higgs

Bibliographic record

VenueTransactions of the American Fisheries Society · 2013
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine animal studies overview
Canadian institutionsMinistry of Natural Resources and ForestryLaurentian UniversityFisheries and Oceans Canada
FundersNatural Resources Canada
KeywordsBiologyMatingFish <Actinopterygii>FisheryEcology

Abstract

fetched live from OpenAlex

Abstract Acoustic communication is central to the reproductive ecology of many fish species, particularly when conditions prevent the use of visual mating cues. The Burbot Lota lota is a freshwater codfish that spawns in a light‐limited, under‐ice environment. Both sexes possess swim bladder muscles, suggesting that both sexes engage in vocalization and that auditory cues are important to their mating system, but research on acoustic communication has been very limited in this species. In the current study we assessed the hearing sensitivity of Burbot from different size‐classes. Burbot hearing was found to be more sensitive in juveniles than in adults, but across size‐classes it was most sensitive at lower frequencies, which is similar to results with other codfishes and corresponds to the sounds produced by gadoids. Anthropogenic noise has the potential to disturb fish. The information gained in this study can be useful in assessing the impact of such noise, particularly under ice cover when Burbot are spawning. Further research is required to determine whether winter‐based resource development activities that generate under‐ice noise are disruptive to Burbot communication and reproduction.

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)0.000
metaresearch head score (Gemma)0.001
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 score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.016
GPT teacher head0.213
Teacher spread0.197 · 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

Citations7
Published2013
Admission routes2
Has abstractyes

Explore more

Same venueTransactions of the American Fisheries SocietySame topicMarine animal studies overviewFrench-language works237,207