Risk screening of non‐native freshwater fishes in <scp>C</scp>roatia and <scp>S</scp>lovenia using the <scp>F</scp>ish <scp>I</scp>nvasiveness <scp>S</scp>creening <scp>K</scp>it
Bibliographic record
Abstract
Abstract The Fish Invasiveness Screening Kit (FISK) version 2 was used to assess the invasiveness potential of 40 introduced and translocated freshwater fish species to Croatia and Slovenia. Based on a priori classification of invasiveness, receiver operating characteristic analysis of FISK scores from two independent assessors resulted in a statistically significant calibration threshold of 11.75. This indicated that FISK was able to discriminate reliably between non‐native species likely to pose a high risk of being invasive and those likely to pose a medium or low risk of invasiveness. Seven species were categorised as ‘medium risk’ and the other 33 as ‘high risk’, whereas no species was categorised as ‘low risk’. The two highest scoring species were European catfish Silurus glanis and North African catfish Clarias gariepinus. Mean scores for all species classified a priori as invasive were ranked as ‘high risk’ sensu lato and fell into the ‘moderately high risk’ subcategory. FISK proved to be a valid tool for assessing the risks posed by non‐native fishes in Croatia and Slovenia. For this reason, it can be adopted as a reliable tool for the prevention of new translocations or introductions of potentially invasive species in the risk assessment area, as well as to assist in decisions regarding future management (i.e. monitoring, control and eradication) and conservation strategies.
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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 | 0.002 | 0.001 |
| 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 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".