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Record W3107944739 · doi:10.3389/fmars.2020.483993

Assessments of 16 Exploited Fish Stocks in Chinese Waters Using the CMSY and BSM Methods

2020· article· en· W3107944739 on OpenAlexaff
Lu Zhai, Cui Liang, Daniel Pauly

Bibliographic record

VenueFrontiers in Marine Science · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine and fisheries research
Canadian institutionsUniversity of British ColumbiaFisheries and Oceans Canada
FundersMinderoo FoundationOak FoundationNational Natural Science Foundation of ChinaBloomberg PhilanthropiesMarisla FoundationNational Natural Science Foundation of China-Shandong Joint FundChina Scholarship CouncilDavid and Lucile Packard Foundation
KeywordsOverfishingSardineFishingFisheryBiomass (ecology)Stock assessmentFish stockEnvironmental scienceMaximum sustainable yieldSustainable yieldFish <Actinopterygii>GeographyEcologyFisheries managementBiology

Abstract

fetched live from OpenAlex

Sixteen marine fish species (populations) exploited by Chinese fisheries were assessed, using published time series of catch and the CMSY and BSM methods. Given the catch times series as inputs, some ancillary information and reasonable constraints, carrying capacity, maximum sustainable yield, and likely time series of biomass and exploitation rate were estimated. The results show that one (7%) of the assessed species was severely depleted, four species (27%) were fully/overfished, six (40%) were outside of safe biological limits, one species (7%) was recovering and three species (20%) were in a healthy state at the end year of their assessment. However, one species, Pacific sardine ( Sardinops sagax ), could not be assessed using CMSY, as the exceedingly large fluctuations of its biomass were mainly environmentally driven. These results correspond with previous knowledge on the status of fish populations along the coast of China, where overfishing is rampant. Based on these assessments, some of the benefits that would result from a reduction of the excessive fishing effort are outlined.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.185
Threshold uncertainty score0.436

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.002
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 head0.031
GPT teacher head0.345
Teacher spread0.314 · 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

Citations38
Published2020
Admission routes1
Has abstractyes

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