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Record W2616946381 · doi:10.1163/22116001-03101002

Canada–U.S. Fisheries Management in the Gulf of Maine: Taking Stock and Charting Future Coordinates in the Face of Climate Change

2017· article· en· W2616946381 on OpenAlexaboutno aff
David VanderZwaag, Megan Bailey, Nancy L. Shackell

Bibliographic record

VenueOcean Yearbook Online · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicArctic and Russian Policy Studies
Canadian institutionsnot available
Fundersnot available
KeywordsStock (firearms)Climate changeOceanographyFisheries managementFisheryGeographyFace (sociological concept)Environmental scienceGeologyFishingSociologyBiology

Abstract

fetched live from OpenAlex

Climate change and ocean acidification are the biggest non-fisheries threats to marine organisms across the global oceans. But where fish stocks are shared be- tween different countries, these oceanographic changes can have consequenc- es for governance regimes and extractive marine activities through changes in stock distribution, and the affording of fishing and access rights. The Gulf of Maine is considered a single ecosystem that is rapidly warming and undergoing ecosystem change. It is also bisected by an international maritime boundary, known as the Hague Line, separating the exclusive economic zones (EEZs) of Canada and the United States. Several fish species, such as cod, haddock, flounders, halibut, American eel, sandlance, cusk, pollock, herring, mackerel, and dogfish straddle the Line and although scientists suspect species’ distributional shifts in relation to the Line due to natural fluctuations and anthropogenic disturbances such as climate change, the full impact and extent of these shifts are not completely known and in some ways are unpredictable.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.043
Threshold uncertainty score0.312

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.007
Science and technology studies0.0030.001
Scholarly communication0.0030.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0110.002

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 head0.039
GPT teacher head0.316
Teacher spread0.277 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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

Citations7
Published2017
Admission routes1
Has abstractyes

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