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Record W4281769922 · doi:10.1134/s199508292204040x

Factors Inducing the Crayfish Procambarus clarkii Invasion and Loss of Diversity in Caohai Wetland

2022· article· en· W4281769922 on OpenAlexaff
Qing Li, Bin He, Yong‐Xiang Chen, Haitao Zhao, Xiaohui Xue, Tu Feng

Bibliographic record

VenueInland Water Biology · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicCrustacean biology and ecology
Canadian institutionsCarleton University
Fundersnot available
KeywordsCrayfishBiologyProcambarus clarkiiSwampEcologyOmnivoreShellfishWetlandIntroduced speciesInvasive speciesAquatic animalFisheryPredationFish <Actinopterygii>

Abstract

fetched live from OpenAlex

Abstract Through comparing three survey reports of Caohai since 1986 once a decade in the past thirty years, we confirm that some physical and chemical factors directly (e.g. a decrease of water salinity, pH variability) have provided opportunities for the invasion of Procambarus clarkii (Girard, 1852) in Caohai (a wetland situated in Guizhou, South-West China) in 2010. In addition, some physical and chemical factors (e.g. an increase of organic oxygen consumption and total nitrogen) reflect the richness of herbivorous food (vascular plants residues) for this exotic crayfish. Furthermore, we suggest that the successful invasion of exotic crayfish is related to human activity and the presence of Anatidae waterflows. Direct and indirect effects of crayfish invasion on Caohai have been evaluated by comparing data between before 2010 and after 2010. Although it is an omnivorous species, eating animal food is more conducive to its growth and reproduction. Invasion of red swamp crayfish had a negative effect on submerged plants, Annelida, aquatic insects and amphibians. By contrast, increases in this exotic crayfish provides a richer food source for Ciconiifornis birds. Further research is needed to solve crayfish invasion thoroughly.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.654

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.002
Research integrity0.0000.000
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 head0.033
GPT teacher head0.224
Teacher spread0.192 · 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

Citations9
Published2022
Admission routes1
Has abstractyes

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