Revisions of the Fish Invasiveness Screening Kit (FISK) for its Application in Warmer Climatic Zones, with Particular Reference to Peninsular Florida
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
The initial version (v1) of the Fish Invasiveness Scoring Kit (FISK) was adapted from the Weed Risk Assessment of Pheloung, Williams, and Halloy to assess the potential invasiveness of nonnative freshwater fishes in the United Kingdom. Published applications of FISK v1 have been primarily in temperate-zone countries (Belgium, Belarus, and Japan), so the specificity of this screening tool to that climatic zone was not noted until attempts were made to apply it in peninsular Florida. To remedy this shortcoming, the questions and guidance notes of FISK v1 were reviewed and revised to improve clarity and extend its applicability to broader climatic regions, resulting in changes to 36 of the 49 questions. In addition, upgrades were made to the software architecture of FISK to improve overall computational speed as well as graphical user interface flexibility and friendliness. We demonstrate the process of screening a fish species using FISK v2 in a realistic management scenario by assessing the Barcoo grunter Scortum barcoo (Terapontidae), a species whose management concerns are related to its potential use for aquaponics in Florida. The FISK v2 screening of Barcoo grunter placed the species into the lower range of medium risk (score = 5), suggesting it is a permissible species for use in Florida under current nonnative species regulations. Screening of the Barcoo grunter illustrates the usefulness of FISK v2 as a proactive tool serving to inform risk management decisions, but the low level of confidence associated with the assessment highlighted a dearth of critical information on this species.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 it