Risk assessment: Cornerstone of an aquatic invasive species program
Bibliographic record
Abstract
Understanding the biological and socio-economic risks associated with existing and potential aquatic invasive species is essential for an aquatic invasive species program to be successful. Effective programs are based on risk analyses, in which risk assessment informs risk management and both are communicated to resource managers and the public. Risk assessments provide valuable information that can be applied to many areas of an aquatic invasive species program. Based on biological and socio-economic risk assessments, appropriate risk management actions related to prevention, early detection and rapid response, and control can be undertaken. In particular, biological risk assessments inform both socio-economic risk assessment and subsequent preventative, monitoring, and control management actions. The uncertainty and knowledge gaps identified in risk assessments help identify and prioritize future research. Risk assessments are used to identify the riskiest aquatic invasive species and pathways and can be used to identify effective management, policy, and legislative actions to minimize risk. This, in turn, allows for the optimal allocation of limited resources to combat aquatic invasive species; therefore, risk assessment should be considered the cornerstone of a successful aquatic invasive species program. This article describes the risk analysis of aquatic invasive species, with emphasis on biological risk assessment and how they can be managed using marine and freshwater examples, with particular emphasis on the risk assessment of Bigheaded Carps in North America.
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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 | 0.016 | 0.027 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".