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Record W4365794403 · doi:10.1139/facets-2022-0164

A prospective framework to support climate-adaptive fisheries in Canada

2023· article· en· W4365794403 on OpenAlexaffvenueabout
Daniel G. Boyce, Nancy L. Shackell, Phil Greyson, B. J. W. Greenan

Bibliographic record

VenueFACETS · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine and fisheries research
Canadian institutionsBedford Institute of OceanographyFisheries and Oceans Canada
Fundersnot available
KeywordsClimate changeVulnerability (computing)Fisheries managementAdaptation (eye)FishingEnvironmental resource managementFisheryAdaptive capacityBusinessAdaptive managementClimate change adaptationEnvironmental planningGeographyEnvironmental scienceEcologyComputer science

Abstract

fetched live from OpenAlex

Climate change affects virtually all marine life and is increasingly a dominant concern for fisheries, reinforcing the need to incorporate climate variability and change when managing fish stocks. Canada is expected to experience widespread climate-driven impacts on its fisheries but does not yet have a clear adaptation strategy. Here, we provide an overview of a project we are developing, the Climate Adaptation Framework for Fisheries, to address this need and support climate adaptation in Canadian marine fisheries. The framework seeks to quantitatively and flexibly evaluate species, fishing infrastructure, and the management and operation of fisheries to assess climate vulnerability comprehensively and provide outputs that can support climate adaptation planning across different sectors, agencies, and stakeholders. This new framework should allow future climate scenarios to be evaluated and identify actionable climate vulnerabilities related to the management of fisheries, creating a systematic approach to supporting climate adaptation in Canada’s fisheries.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.258
Threshold uncertainty score0.999

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 science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0130.001

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 head0.019
GPT teacher head0.251
Teacher spread0.232 · 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; both teacher heads agree on what is shown here.

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
Published2023
Admission routes3
Has abstractyes

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