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Record W2886446460 · doi:10.1126/sciadv.aat8351

High seas fisheries play a negligible role in addressing global food security

2018· article· en· W2886446460 on OpenAlexaff
Laurenne Schiller, Megan Bailey, Jennifer Jacquet, Enric Sala

Bibliographic record

VenueScience Advances · 2018
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine and fisheries research
Canadian institutionsFisheries and Oceans CanadaDalhousie University
FundersNational Geographic SocietyPew Charitable Trusts
KeywordsFood securityFisheryBusinessFisheries scienceEnvironmental scienceFisheries managementFishingBiologyEcologyAgriculture

Abstract

fetched live from OpenAlex

Recent international negotiations have highlighted the need to protect marine diversity on the high seas-the ocean area beyond national jurisdiction. However, restricting fishing access on the high seas raises many concerns, including how such restrictions would affect food security. We analyze high seas catches and trade data to determine the contribution of the high seas catch to global seafood production, the main species caught on the high seas, and the primary markets where these species are sold. By volume, the total catch from the high seas accounts for 4.2% of annual marine capture fisheries production and 2.4% of total seafood production, including freshwater fisheries and aquaculture. Thirty-nine fish and invertebrate species account for 99.5% of the high seas targeted catch, but only one species, Antarctic toothfish, is caught exclusively on the high seas. The remaining catch, which is caught both on the high seas and in national jurisdictions, is made up primarily of tunas, billfishes, small pelagic fishes, pelagic squids, toothfish, and krill. Most high seas species are destined for upscale food and supplement markets in developed, food-secure countries, such as Japan, the European Union, and the United States, suggesting that, in aggregate, high seas fisheries play a negligible role in ensuring global food security.

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 categoriesScience and technology studies, Insufficient 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 score0.453
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.003
Scholarly communication0.0000.002
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.015
GPT teacher head0.289
Teacher spread0.274 · 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.

Study designObservational
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

Citations87
Published2018
Admission routes1
Has abstractyes

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