Application of FISK, an Invasiveness Screening Tool for Non‐Native Freshwater Fishes, in the Murray‐Darling Basin (Southeastern Australia)
Bibliographic record
Abstract
The Fish Invasiveness Scoring Kit (FISK) is currently one of the most popular pre-screening tools for freshwater fishes. A recent upgrade has ensured its wider climatic relevance to countries with subtropical regions. This enhancement is of particular importance to Australia, which encompasses tropical, arid, and temperate zones, and where the introduction of non-native fish species poses a significant risk to biodiversity. In this study, 55 fish species previously evaluated in a U.K.-based calibration of FISK are reassessed for their potential invasiveness in the Murray-Darling Basin (MDB; southeastern Australia), the continent's largest catchment encompassing arid and temperate climates. Approximately half of the species were classed as "medium risk" and the other half as "high risk," and the ≥19 threshold previously identified from the calibration study was confirmed. The three highest scoring species (common carp Cyprinus carpio carpio, goldfish Carassius auratus, and eastern mosquitofish Gambusia holbrooki) were those already present and invasive in the area, whereas nearly half of the tropical and subtropical species had lower scores compared to U.K. assessments, possibly because of climate change predictions of drier conditions across the MDB. There were some discordances between FISK and two Australian-based assessment protocols, one of which is qualitative and the other represents a simplified version of FISK. Notably, the Australian origins of FISK should provide for an additional reason for further applications of the tool in other RA areas (i.e., drainage basins) of the continent, ultimately encouraging adoption as the country's reference screening tool for management and conservation purposes.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.000 | 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".