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Record W2085616841 · doi:10.1051/kmae/2012025

Aboriginal fisher perspectives on use of biotelemetry technology to study adult Pacific salmon

2012· article· en· W2085616841 on OpenAlexafffundabout
Vivian M. Nguyen, Graham D. Raby, S. G. Hinch, Steven J. Cooke

Bibliographic record

VenueKnowledge and Management of Aquatic Ecosystems · 2012
Typearticle
Languageen
FieldEnvironmental Science
TopicFish Ecology and Management Studies
Canadian institutionsUniversity of British ColumbiaCarleton University
FundersFisheries and Oceans CanadaNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsBiotelemetryTelemetryOutreachFisheryStakeholderApprehensionFish <Actinopterygii>Environmental resource managementGeographyPublic relationsBiologyEngineeringTelecommunicationsPsychologyPolitical scienceEnvironmental science

Abstract

fetched live from OpenAlex

Biotelemetry has become a popular tool accepted by the scientific community as a reliable approach for studying wild fish. However, stakeholder perspectives on scientific techniques and the information they generate are not uniformly positive. Aboriginal groups in particular may have opposition or apprehension to telemetry as a research tool. To that end, we conducted a river-bank survey of 111 aboriginal First Nations fishers that target adult Pacific salmon in the lower Fraser River, British Columbia, Canada. The majority of respondents had heard of telemetry, but few had knowledge of its function. Most responses regarding the use of telemetry in fisheries science were positive. The few negative perspectives were primarily concerned about the effects of tagging procedures whereas positive perspectives arose because telemetry was perceived to generate information on migration patterns and survival. Over half of the respondents would trust data arising from telemetry studies, but some had conditions related to the group conducting the research and their experience with fish handling. Several respondents noted the need for additional consultation and outreach with aboriginal communities (especially fishers) to better inform them of study questions and techniques which, in the case of telemetry studies, could promote better participation in tag return programs and uptake of knowledge emanating from use of telemetry.

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 score0.086
Threshold uncertainty score0.629

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.012
GPT teacher head0.267
Teacher spread0.254 · 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

Citations6
Published2012
Admission routes3
Has abstractyes

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