Risk screening of the potential invasiveness of non-native freshwater fishes in the River Ob basin (West Siberian Plain, Russia)
Bibliographic record
Abstract
Abstract To inform regional managers of potentially invasive non-native (NN) freshwater fishes in the principal hydrosystem that drains the West Siberian Plain, the River Ob basin, 31 extant and potential future NN fish species were screened using the Aquatic Species Invasiveness Screening Kit (AS-ISK) with respect to current and projected future climate conditions. Calibration of the AS-ISK scores, using receiver operating characteristic curve analysis, identified ‘basic risk assessment’ and ‘climate change assessment’ threshold scores of 27.5 and 34.75, respectively, with which to distinguish species that pose a high risk of being invasive in the Ob basin and those that pose a low-to-medium risk. Of the species screened, 12 ranked as high risk (black bullhead Ameiurus melas , brown bullhead Ameiurus nebulosus , grass carp Ctenopharyngodon idella , common carp Cyprinus carpio , eastern mosquitofish Gambusia holbrooki , silver carp Hypophthalmichthys molitrix , oriental weatherfish Misgurnus anguillicaudatus , rainbow trout Oncorhynchus mykiss , Chinese (Amur) sleeper Perccottus glenii , topmouth gudgeon Pseudorasbora parva , brown trout Salmo trutta , pikeperch Sander lucioperca and rudd Scardinius erythrophthalmus ). The remaining species ranked as medium or low risk. Although the risk scores increased in 68% of species under climate change conditions, this affected the risk rankings of only two species: Salmo trutta decreased in rank from high to medium and Sander lucioperca increased in rank from medium to high. The outcomes of the present study, which identified 12 species for which full risk assessments are recommended, serves to inform the development of NN species policy and management in Russia.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 teacher head, 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".