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Record W4415394570 · doi:10.1139/cjfas-2025-0213

Once considered a “disruptive science”, biotelemetry is now among the most trusted and relevant approaches informing salmon fisheries management

2025· article· en· W4415394570 on OpenAlexaffvenue
Scott G. Hinch, Steven J. Cooke, Nathan Young

Bibliographic record

VenueCanadian Journal of Fisheries and Aquatic Sciences · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicWater Quality Monitoring Technologies
Canadian institutionsUniversity of OttawaCarleton UniversityAgricultural Institute of CanadaUniversity of British Columbia
Fundersnot available
KeywordsBiotelemetryResource (disambiguation)Fisheries scienceResource management (computing)Fisheries managementTelemetry

Abstract

fetched live from OpenAlex

New scientific approaches that prompt a rethink in resource policy or management, and that lead to “worldviews” being challenged, are considered to be “disruptive”. This paper explores (i) the complex 30-year history of a biotelemetry science “disruption” with the management of Fraser River salmon fisheries, (ii) the transformation of biotelemetry science from a disruptive to an accepted science, and (iii) the circumstances that have now made it one of the most important tools for managing Pacific salmon fisheries. We conclude with an overview of a successful case study involving the co-production of biotelemetry science to inform the management of the British Columbia marine recreational fishery. The approach we advocate for provides a pathway for all telemetry practitioners to avoid potential pitfalls and take advantage of what we have learned to ensure biotelemetry science continues to have the potential to generate relevant knowledge to inform management of all socio-economically important fishes.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.108
Threshold uncertainty score0.993

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.010
Scholarly communication0.0010.001
Open science0.0010.000
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 head0.035
GPT teacher head0.237
Teacher spread0.202 · 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.

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 routes2
Has abstractyes

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