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
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 it