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Record W4410079108 · doi:10.1093/fshmag/vuaf042

Fish(eries) management on the third rail: Defusing and advancing the dialogue on hatcheries and stocking for enhancement, conservation, and restoration through an innovative roundtable

2025· article· en· W4410079108 on OpenAlexaff
Hannah L. Harrison, Seth M. White, Neil R. Loneragan, Joy Hazell, Kai Lorenzen

Bibliographic record

VenueFisheries · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicFish Ecology and Management Studies
Canadian institutionsDalhousie University
Fundersnot available
KeywordsStockingFish <Actinopterygii>FisheryBusinessNature ConservationRestoration ecologyGeographyEnvironmental ethicsPolitical scienceEnvironmental planningEcologyBiologyPhilosophy

Abstract

fetched live from OpenAlex

ABSTRACT This feature reports on a roundtable session on aquaculture-aided fisheries enhancement, restoration, and conservation that was held at the 2024 Ninth World Fisheries Congress. The session aimed to foster constructive dialogue on the use of hatcheries and stocking programs for conservation purposes, a topic sometimes referred to as the “third rail” of fisheries management. The standing room-only session drew participants from around the world and a wide variety of aquaculture contexts. Using pre-session reflections, in-session discussions, and real-time feedback tools, the session encouraged open discussions on key challenges and promising practices and policies within ­aquaculture-aided enhancement, restoration, and conservation. Participants identified both established challenges and novel perspectives, particularly with respect to the sociocultural aspects of hatchery practices and the need for policies that acknowledge local contexts. The organizers considered the session to be a step forward in developing a robust and diverse community of practice interested in aquaculture-aided approaches.

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 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 score0.249
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 studies0.0010.000
Scholarly communication0.0000.001
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 head0.027
GPT teacher head0.261
Teacher spread0.234 · 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

Citations3
Published2025
Admission routes1
Has abstractyes

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