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Record W3006088137 · doi:10.1111/conl.12708

Shark fin trade bans and sustainable shark fisheries

2020· article· en· W3006088137 on OpenAlex
Francesco Ferretti, David Jacoby, Mariah O. Pfleger, Timothy D. White, Felix Dent, Fiorenza Micheli, Andrew A. Rosenberg, Larry B. Crowder, Barbara A. Block

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

VenueConservation Letters · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicIchthyology and Marine Biology
Canadian institutionsNova Scotia Department of Agriculture
FundersFondation Bertarelli
KeywordsFisheries managementFisheryBusinessFinFishingBiologyEngineering

Abstract

fetched live from OpenAlex

Abstract The U.S. Congress is currently discussing the Shark Fin Sales Elimination Act to eliminate shark fin trade at the federal level. This bill was introduced in 2017 and has been proceeding very slowly in Congress because of mixed reviews from the scientific community. Debate exists on whether shark conservation and management are effectively addressed with tightened trade controls for imported shark products or blanket bans that outright end U.S. participation in the shark fin trade. Here we contribute to this debate with a review and analysis of economic, nutritional, ethical, and legal arguments, as well as of the shark fisheries status and shark fin trade. We show that the United States has a limited commercial interest in shark fisheries and contributes to the shark fin trade mainly as a facilitator. A fin trade ban has few tangible economic drawbacks but would have a considerable conservation impact. While making all shark fisheries sustainable is the ultimate goal, in practice this objective is far from achievable everywhere in the world. Conversely, banning shark fin trade is an interim measure that nations like the United States can take with negligible cost and can truly impact the biggest driver of shark exploitation globally.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.099
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
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.012
GPT teacher head0.189
Teacher spread0.177 · 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