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Record W4416876160 · doi:10.1139/facets-2025-0163

Opportunities and challenges for Canada’s mariculture under climate change: a regional and sectoral outlook

2025· article· en· W4416876160 on OpenAlexafffundvenueabout
Muhammed A. Oyinlola, William W. L. Cheung

Bibliographic record

VenueFACETS · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine and fisheries research
Canadian institutionsUniversity of British Columbia
FundersSocial Sciences and Humanities Research Council of CanadaNatural Sciences and Engineering Research Council of Canada
KeywordsMaricultureClimate changeProduction (economics)Investment (military)IndigenousAdaptation (eye)Global warmingHabitat

Abstract

fetched live from OpenAlex

Mariculture is an important component of the seafood industry in Canada, but climate change presents both risks and opportunities. Using the Global Mariculture Production Model (GOMAP), an integrated framework that projects production potential under combined biophysical and socio-economic constraints, we assessed future changes in mariculture production, farm-gate prices, and employment for 13 key species across Canada’s Atlantic and Pacific coasts. Scenarios included two climate pathways (SSP1-2.6 and SSP5-8.5) and three production approaches. Results show substantial regional contracts with important implications for spatial planning and adaptation policy. The Atlantic region shows modest gains under SSP1-2.6 but steep declines under SSP5-8.5 due to climate stress and habitat loss. In contrast, Pacific Canada, particularly central and northern areas, shows strong growth potential under SSP5-8.5, driven by climate-resilient species like Coho salmon and steelhead trout. However, these gains depend on policy, infrastructure, and spatial planning that reconcile mariculture expansion with conservation, Indigenous uses, and other priorities. Rising farm-gate prices may benefit producers but threaten affordability while employment outcomes diverge, with consistent job losses in Atlantic Canada but potential gains in the Pacific. Our findings highlight the need for region-specific adaptation strategies and investment in sustainable and resilient farming systems.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.650
Threshold uncertainty score0.951

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 head0.116
GPT teacher head0.276
Teacher spread0.160 · 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 designNot applicable
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 routes4
Has abstractyes

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