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Record W4402564052 · doi:10.1016/j.isci.2024.110986

Global sustainability assessment of cephalopod fisheries based on pressure-state-response framework

2024· article· en· W4402564052 on OpenAlexaff
Daomin Peng, Honghong Liu, Wenjie Zhang, Lu Xu, Ruhao Jiang, Yugui Zhu, Iria García–Lorenzo, Jiansong Chu, U. Rashid Sumaila

Bibliographic record

VenueiScience · 2024
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicCephalopods and Marine Biology
Canadian institutionsUniversity of British Columbia
FundersNational Natural Science Foundation of China
KeywordsSustainabilityCephalopodFisheryBusinessEcologyBiology

Abstract

fetched live from OpenAlex

Cephalopods are growing in commercial importance due to their unique biological characteristics; however, uncertainty about the pressure facing cephalopod fisheries poses a challenge to the health of fisheries and to policy development. Therefore, identifying and quantifying the dynamics of the sustainability of global cephalopod fisheries becomes critical. This study focuses on twenty major cephalopod producing countries around the world, using a pressure-state-response (PSR) framework together with an uncertainty assessment methodology. The results suggest that the sustainability of cephalopod fisheries varies greatly among countries; with developed countries characterized by low pressure and high response while developing countries show the opposite characteristics. Although there were large differences in the sustainability of this fishery among countries, the level of sustainability tightened slightly over time. The results emphasize that key response indicators, such as mitigating stressors on fisheries and improving the governance capacity of government departments, contribute to the sustainable use of cephalopod resources.

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)0.001
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.525
Threshold uncertainty score0.384

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.286
Teacher spread0.276 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations11
Published2024
Admission routes1
Has abstractyes

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