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Record W4390475265 · doi:10.5376/ijms.2024.14.0001

Interpretation of Ensemble Forecasting Study on the Response of Fish Distribution in the Yellow and Bohai Seas of China to Climate Change

2024· article· en· W4390475265 on OpenAlexvenueno aff
Jinni Wu

Bibliographic record

VenueInternational Journal of Marine Science · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine and fisheries research
Canadian institutionsnot available
Fundersnot available
KeywordsClimate changeDistribution (mathematics)ChinaRange (aeronautics)Fish <Actinopterygii>Species distributionMarine fishHabitatGeographyEnvironmental scienceClimatologyEcologyEnvironmental resource managementFisheryBiologyMathematicsGeology

Abstract

fetched live from OpenAlex

This article provides an academic review of the research titled &quot;Ensemble projections of fish distribution in response to climate changes in the Yellow and Bohai Seas, China&quot; Ensemble prediction of fish distribution in response to climate change in the Yellow and Bohai Seas. In order to determine the geographical distribution pattern and potential suitable habitats for fish in the Yellow and Bohai Seas, this study established a spatial distribution set model for 22 important fish species using 3185 valid distribution records extracted from multiple databases and 9 environmental variables. The research results provide a theoretical basis for predicting climate driven changes in the range of fish activity in one of the most severely affected marine ecosystems in the world, and can be extended to developing climate adaptive management strategies. This review mainly summarizes the main contents and innovations of the study, puts forward academic suggestions for the future research direction of the study, and quantifies the impact of climate change on marine Species distribution.

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)0.004
metaresearch head score (Gemma)0.001
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 score0.310
Threshold uncertainty score0.131

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
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 head0.033
GPT teacher head0.312
Teacher spread0.279 · 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

Citations1
Published2024
Admission routes1
Has abstractyes

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