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Record W3027200232 · doi:10.3389/fmars.2020.00298

Construction and Interpretation of Particle Size Distribution Spectra From 19 Ecopath Models of Chinese Coastal Ecosystems

2020· article· en· W3027200232 on OpenAlexaff
Lu Zhai, Daniel Pauly

Bibliographic record

VenueFrontiers in Marine Science · 2020
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicMarine and coastal ecosystems
Canadian institutionsUniversity of British ColumbiaFisheries and Oceans Canada
FundersBloomberg PhilanthropiesPaul M. Angell Family FoundationMarisla FoundationMinderoo FoundationOak FoundationChina Scholarship CouncilDavid and Lucile Packard Foundation
KeywordsInterpretation (philosophy)EcosystemEnvironmental scienceOceanographyDistribution (mathematics)GeographyEcologyAtmospheric sciencesGeologyComputer scienceBiologyMathematics

Abstract

fetched live from OpenAlex

To assess the changes that fisheries have imposed on the functioning of coastal marine ecosystems in China, nineteen published Ecopath models were used to construct particle size distribution (PSD) spectra. The results show that high biomass of jellyfish from ranching operation impacted almost all of the ecosystems studied here. As well, an increasing impact of fisheries was demonstrated, via steeper PSD slopes, for ecosystems with models covering two or more periods. Models of nearshore areas, i.e., bays and estuaries, exhibited steeper PSD slopes that models of offshore areas. The PSD slopes were also correlated with total catch (TC), with the mean trophic level of catch and with the Shannon-Wiener diversity index (H’) that can be computed from Ecopath models. A multiple regression predicting the PSD slopes from year and mean trophic level of the catch explained 38.5 % of the variance in the slopes. Overall, this study confirmed the status of depletion in China’s marine fisheries resources. Also, it established that including a PSD while constructing an Ecopath model, which is straightforward, will improve it, and allow more insights to be obtained from it regarding the impact of fishing on marine ecosystems.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.678
Threshold uncertainty score0.788

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.005
GPT teacher head0.182
Teacher spread0.176 · 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

Citations7
Published2020
Admission routes1
Has abstractyes

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