Invasiveness screening of non‐native fishes for the middle reach of the Yarlung Zangbo River, Tibetan Plateau, China
Bibliographic record
Abstract
Abstract The aim of this study was to identify potentially invasive non‐native freshwater fishes in the middle reach of the Yarlung Zangbo River, Tibetan Plateau (China), using the Aquatic Species Invasiveness Screening Kit (AS‐ISK), as decision‐support tool. Based on independent evaluations of 24 non‐native freshwater fishes, receiver operating curve analysis identified a threshold score of ≥29 for distinguishing species likely to pose a high risk of becoming invasive from species likely to pose low‐to‐medium risk (<29) in the risk assessment area. Nine species were categorized as “high risk”: goldfish Carassius auratus, topmouth gudgeon Pseudorasbora parva, brook trout Salvelinus fontinalis, Oriental weatherfish (a.k.a. dojo gudgeon) Misgurnus anguillicaudatus, Siberian taimen Hucho taimen, common carp Cyprinus carpio, peled Coregonus peled, western mosquitofish Gambusia affinis, and Chinese rice fish Oryzias sinensis. The three lowest scoring species were Arctic cisco Coregonus autumnalis, Wuchang bream Megalobrama amblycephala, and Chinese ice fish Neosalanx taihuensis, which are unlikely to be invasive because they are unable to complete their life cycle in the risk assessment area. Climate change assessments scores increased or remained the same for warm‐water species and decreased for coldwater species. This study was the first application of AS‐ISK in western China, and the results suggest that AS‐ISK is a useful and valid tool for identifying potentially invasive risk aquatic species in China.
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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 | 0.001 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".