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Canada–USA Bilateral Fisheries Management in the Gulf of Maine: Under the Radar Screen

2007· article· en· W3124733312 on OpenAlexaffabout
Emily J. Pudden, David VanderZwaag

Bibliographic record

VenueReview of European Community & International Environmental Law · 2007
Typearticle
Languageen
FieldEnvironmental Science
TopicInternational Maritime Law Issues
Canadian institutionsDalhousie University
Fundersnot available
KeywordsGroundfishFisheries managementBusinessFishingWork (physics)EnforcementFisheryFish stockPolitical scienceEnvironmental resource managementEnvironmental planningGeographyEnvironmental scienceEngineering

Abstract

fetched live from OpenAlex

Canada and the USA have developed a series of cooperative initiatives that address transboundary fisheries issues in the Gulf of Maine. The Canada–USA Steering Committee serves as an umbrella forum for discussing and coordinating transboundary management measures. Through the work of the Transboundary Resource Assessment Committee and the Transboundary Management Guidance Committee, the Steering Committee has overseen the development of joint scientific stock assessments and a sharing agreement for groundfish resources in the vicinity of the eastern Georges Bank. The bilateral Fisheries Enforcement Agreement helps ensure the success of such cooperative management initiatives by combating illegal fishing in the vicinity of the international boundary. However, the largely informal ‘under the radar screen’ arrangements, while positive on many fronts, to date fall short of fully implementing key principles of sustainable development, such as public participation, the ecosystem approach, integration and precaution.

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)0.006
metaresearch head score (Gemma)0.009
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.144
Threshold uncertainty score0.290

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0060.004
Scholarly communication0.0070.002
Open science0.0010.003
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0080.001

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 head0.014
GPT teacher head0.239
Teacher spread0.224 · 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
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

Citations10
Published2007
Admission routes2
Has abstractyes

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