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Record W4366808821 · doi:10.47886/9781934874707.ch20

Freshwater Fisheries in Canada: Historical and Contemporary Perspectives on the Resources and Their Management

2023· book-chapter· en· W4366808821 on OpenAlexaboutno aff

Bibliographic record

VenueAmerican Fisheries Society eBooks · 2023
Typebook-chapter
Languageen
FieldEnvironmental Science
TopicFish Ecology and Management Studies
Canadian institutionsnot available
Fundersnot available
KeywordsOverexploitationFisheries managementFisheries scienceFisheries lawFisherySustainabilityGeographyEnvironmental resource managementFishingEcologyEnvironmental scienceBiology

Abstract

fetched live from OpenAlex

Abstract.—Maintaining the viability and sustainability of freshwater fisheries lies at the heart of the intersection between fisheries science, management, and policy. In response to changing biotic and abiotic drivers, fisheries science has advanced considerably in recent years, becoming more integrative, multi-disciplinary, and diverse. We have not only gained a better understanding of fish, ecosystems, and fisheries, but have started to bridge the gap between science, management, and policy. Despite notable successes of fisheries science and management in Canada, our fisheries face a number of threats, including climate change, invasive species, habitat degradation, regulated rivers and water demands, and overexploitation. How we address these threats will be determined in part by the way we value knowledge from diverse sources. With such uncertainty on the horizon, there is value in taking a philosophical view of the future of freshwater fisheries in Canada. Our objective is to provide an overview of risk factors relevant to Canadian freshwater fisheries from the perspectives of a diverse team of primarily early-career fisheries scientists. We integrate our prognostications to provide an outlook for the future of freshwater fisheries science, management, and policy in Canada.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.570
Threshold uncertainty score1.000

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 studies0.0000.002
Scholarly communication0.0000.000
Open science0.0000.001
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 head0.017
GPT teacher head0.172
Teacher spread0.155 · 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 designNot applicable
Domainnot available
GenreOther

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
Published2023
Admission routes1
Has abstractyes

Explore more

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