MétaCan
Menu
Back to cohort
Record W2999730463 · doi:10.1071/mf18472

Historical dynamics of the demersal fish community in the East and South China Seas

2020· article· en· W2999730463 on OpenAlexaff
Jin Gao, James T. Thorson, Cody Szuwalski, Hui‐Yu Wang

Bibliographic record

VenueMarine and Freshwater Research · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine and fisheries research
Canadian institutionsFisheries and Oceans CanadaMemorial University of Newfoundland
Fundersnot available
KeywordsFishingFisheryGeographyOtterDemersal zoneAbundance (ecology)Distribution (mathematics)HabitatEstuaryChinaEcologyOceanographyBiology

Abstract

fetched live from OpenAlex

Taiwan has a long history of fishery operations and contributes significantly to the global fishery harvest. The East and South China seas are important fishing grounds for which publicly available data are very limited. More efforts are needed to digitise and analyse historical catch rate data to illuminate species and community changes in this region. In this study we digitised historical records of catch and effort from government fishery reports for nine commercial species caught by otter trawl, and reported quarterly from 1970 to 2001, from the East and South China seas. We analysed the four seasons and present abundance indices, distributions and among-species correlations for nine commercially important species from 1970 to 1988 (a period with high fishing effort) using a multispecies spatiotemporal model that estimates both covariation in multispecies catch rates, attributed to spatial habitat preferences and environmental responses, and indices representing trends in abundance and distribution. We found substantial spatial, temporal and spatiotemporal variation in the distribution of fishes and season-specific patterns. We recommend collaborative work from various adjacent countries to digitise historical records of fishing catch rates, because more records would potentially address scientific disagreements regarding trends in the abundance and distribution of commercial fishes in this region through comparative studies.

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 score0.129
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.003
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.064
GPT teacher head0.267
Teacher spread0.203 · 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

Citations6
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueMarine and Freshwater ResearchSame topicMarine and fisheries researchFrench-language works237,207