Risk screening of non‐native, translocated and traded aquarium freshwater fishes in <scp>G</scp>reece using <scp>F</scp>ish <scp>I</scp>nvasiveness <scp>S</scp>creening <scp>K</scp>it
Bibliographic record
Abstract
Abstract The invasion of non‐indigenous freshwater fish species is one of the most important threats to aquatic biodiversity. Similar to other Mediterranean countries, Greece is considered a hot spot for freshwater biodiversity, with many range‐restricted endemics of high conservation concern. The aim of this study was to undertake a risk screening assessment to evaluate the invasive potential of non‐native, translocated and traded aquarium fishes in Greece by applying the Fish Invasiveness Screening Kit (FISK). In total, 73 freshwater fish species were evaluated by two assessors. FISK was able to discriminate reliably between invasive and non‐invasive species with a threshold of 15.25. Based on mean scores, 30 species were classified as ‘high risk’, of which 17 as ‘moderately high risk’, six as ‘high risk’ and seven as ‘very high risk’. There was a high coincidence rate for the species categorisation between the two assessors, but significant differences in certainty. The results suggest that FISK is a useful tool for assessing risks posed by non‐native, translocated and traded aquarium fish species in Greece.
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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 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".