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Record W4412161356 · doi:10.1139/facets-2024-0240

A framework for the use of conservation hatcheries to support wild Pacific salmon recovery in Canada

2025· article· en· W4412161356 on OpenAlexafffundvenueabout
Michael J. Bradford, Lian E. Kwong, Carrie A. Holt, Brock Christopher Ramshaw, Ryan V. Galbraith

Bibliographic record

VenueFACETS · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicFish Ecology and Management Studies
Canadian institutionsFisheries and Oceans Canada
FundersFisheries and Oceans Canada
KeywordsFisheryGeographyOceanographyBiologyGeology

Abstract

fetched live from OpenAlex

Hatcheries have long been used to produce Pacific salmon ( Oncorhynchus spp.), and in recent years, they have been used to assist the recovery of populations that have declined and become imperiled. For populations where the conservation of biodiversity is a primary goal, we used guidance from Canada's Wild Salmon Policy, and recent scientific advice, to develop a framework for the use of hatchery supplementation in the recovery of wild salmon in Canada to manage the tradeoff between the increase in abundance that a hatchery program can provide, with the risks to wild salmon from supplementation. We use a simple deterministic model to show that hatchery supplementation can play a role in boosting abundance during the early phases of recovery of wild salmon populations, but if natural production does not increase, the population may become dominated by hatchery-origin spawners and may be contrary to biodiversity goals. We conclude that in certain circumstances conservation hatchery programs can be an appropriate tool for the recovery of wild salmon populations, but uncertainty about long-term risks to wild populations requires a cautious approach. Careful planning and ongoing monitoring and program adjustment are needed to ensure that adverse impacts are minimized.

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 score0.660
Threshold uncertainty score0.701

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 head0.031
GPT teacher head0.241
Teacher spread0.210 · 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

Citations2
Published2025
Admission routes4
Has abstractyes

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