MétaCan
Menu
Back to cohort
Record W2990013817 · doi:10.3389/fmars.2019.00724

Yield-per-Recruit, Utility-per-Recruit, and Relative Biomass of 21 Exploited Fish Species in China's Coastal Seas

2019· article· en· W2990013817 on OpenAlexaff
Lu Zhai, Daniel Pauly

Bibliographic record

VenueFrontiers in Marine Science · 2019
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine and fisheries research
Canadian institutionsUniversity of British ColumbiaFisheries and Oceans Canada
FundersBloomberg PhilanthropiesPaul M. Angell Family FoundationMarisla FoundationMinderoo FoundationOak FoundationChina Scholarship CouncilDavid and Lucile Packard Foundation
KeywordsOverfishingFishingBiomass (ecology)ChinaFisheryMaximum sustainable yieldYield (engineering)BiologyFisheries managementGeographyEnvironmental scienceEcology

Abstract

fetched live from OpenAlex

Based on growth and related fishery parameters, three approaches, yield-per-recruit(Y/R), utility-per-recruit (U/R) analyses and relative biomass (B/B0) analyses were applied to 21 economically important, trawl-caught species in China’s coastal seas to estimate their relative yield, economic value and biomass under different schedules of fishing mortality and mean length at first capture. The results show that all species suffer from overfishing, given the high average fishing mortality (F~1 year-1) and small mesh size (~1 cm) used by trawlers. Long-term Y/R would double and U/R (expressed as price per landed weight) would increase 5-fold if mesh size were increased to about 10 cm. Comparing Y/R and U/R showed that the benefits of higher prices for larger individuals were detectable only if larger mesh sizes are used, so that individuals are caught only after they have been able to grow. The Y/R analyses also allowed estimating the biomass of the 21 assessed populations relative to their unexploited biomass, i.e., B/B0. Species-specific B/B0 values ranged from 0.01 to 0.58, with a mean of 0.16 (+/- 0.03), i.e., much lower than the 50% reduction corresponding to Maximum Sustainable Yield (i.e., B/BMSY =1), or B/B0 =0.5). This confirms the many authors who reported systematic overfishing along China’s coastlines, and suggests that rebuilding stocks should be the foremost goal of fisheries management in China.

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 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 score0.176
Threshold uncertainty score0.995

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 studies0.0000.002
Scholarly communication0.0000.001
Open science0.0010.002
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.000

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 head0.019
GPT teacher head0.241
Teacher spread0.222 · 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

Citations32
Published2019
Admission routes1
Has abstractyes

Explore more

Same venueFrontiers in Marine ScienceSame topicMarine and fisheries researchFrench-language works237,207