Factors Inducing the Crayfish Procambarus clarkii Invasion and Loss of Diversity in Caohai Wetland
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".