Risk screening of non-native freshwater fishes at the frontier between Asia and Europe: first application in Turkey of the fish invasiveness screening kit
Bibliographic record
Abstract
The aim of the present study was to assess the invasive potential of introduced non-native and translocated fishes in Turkey (Anatolia and Thrace) by applying the Fish Invasiveness Screening Kit (FISK), a risk identification tool for freshwater fishes. From independent evaluations by two assessors of 35 species, calibration of FISK for Turkey identified a threshold score of 23, which reliably distinguished between potentially invasive (high risk) and potentially non-invasive (medium to low risk) fishes for Anatolia (Asia) and Thrace (Europe). No species was categorized as ‘low risk’, 18 species were categorized as ‘medium risk’ and 17 as ‘high risk’ (two being ‘moderately high risk’, nine ‘high risk’, and six ‘very high risk’). The highest scoring species was gibel carp Carassius gibelio, whereas the lowest scoring species was Caucasian dwarf goby Knipowitschia caucasica, a translocated species. Assessor certainty in their responses averaged overall between ‘mostly uncertain’ and ‘mostly certain’, with red piranha Pygocentrus nattereri and topmouth gudgeon Pseudorasbora parva achieving the lowest and highest certainty values, respectively, and with overall significant differences in certainty between assessors. The results of the present study indicate that FISK is a useful and viable tool for identifying potentially invasive non-native fishes in Turkey, a country characterized by natural biogeographical frontiers.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".