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Record W2156890841 · doi:10.1017/s0030605309990470

Conserving wild fish in a sea of market-based efforts

2009· article· en· W2156890841 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueOryx · 2009
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicIdentification and Quantification in Food
Canadian institutionsInStream Fisheries Research (Canada)Fisheries and Oceans CanadaDalhousie UniversityUniversity of British Columbia
FundersPew Charitable Trusts
KeywordsBusinessFishingOverfishingStewardship (theology)Fish stockFishing industryNatural resource economicsConsumption (sociology)FisheryEnvironmental planningMarketingEconomicsGeography

Abstract

fetched live from OpenAlex

Abstract Over the past decade conservation groups have put considerable effort into educating consumers and changing patterns of household consumption. Many groups aiming to reduce overfishing and encourage sustainable fishing practices have turned to new market-based tools, including consumer awareness campaigns and seafood certification schemes (e.g. the Marine Stewardship Council) that have been well received by the fishing and fish marketing industries and by the public in many western countries. Here, we review difficulties that may impede further progress, such as consumer confusion, lack of traceability and a lack of demonstrably improved conservation status for the fish that are meant to be protected. Despite these issues, market-based initiatives may have a place in fisheries conservation in raising awareness among consumers and in encouraging suppliers to adopt better practices. We also present several additional avenues for market-based conservation measures that may strengthen or complement current initiatives, such as working higher in the demand chain, connecting seafood security to climate change via life cycle analysis, diverting small fish away from the fishmeal industry into human food markets, and the elimination of fisheries subsidies. Finally, as was done with greenhouse gas emissions, scientists, conservation groups and governments should set seafood consumption targets.

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.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.175
Threshold uncertainty score0.233

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 head0.011
GPT teacher head0.255
Teacher spread0.244 · 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