An integrated evaluation of the invasiveness risk posed by non-native crayfish in Lake Maggiore (Northwest Italy)
Bibliographic record
Abstract
Risk analysis of non-native species invasions is one of the main challenges currently faced by both scientists and environmental managers.In this study, the three risk screening toolkits Harmonia + , Aquatic Species Invasiveness Screening Kit (AS-ISK) and Canadian Marine Invasive Screening Tool (CMIST) were used in conjunction to evaluate the risk of invasiveness of eight non-native crayfish species (three extant already present in the risk assessment area and five horizon, not yet reported, but likely to arrive in the near future) for Lake Maggiore (Northwest Italy).Based on the toolkit-specific risk scores for each species and their final ranking according to the thresholds set for each toolkit: 1) Harmonia + ranked five species with a medium-risk level of invasiveness and three with a low-risk level; 2) AS-ISK ranked all species as high risk; 3) CMIST ranked six species as high risk and two as medium risk.By combining the risk scores from the three toolkits and setting an ad hoc threshold, extant horizon calico crayfish Faxonius immunis, spinycheek crayfish Faxonius limosus, signal crayfish Pacifastacus leniusculus, red swamp crayfish Procambarus clarkii and marbled crayfish Procambarus virginalis were ranked as high risk, whereas horizon Australian red claw crayfish Cherax quadricarinatus, yabby Cherax destructor and Danube crayfish Pontastacus leptodactylus were ranked as medium risk.It is anticipated that the findings of this study will help inform managers about the proper implementation of non-native species management strategies for the Lake Maggiore watershed.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".