Evidence‐based tool surpasses expert opinion in predicting probability of eradication of aquatic nonindigenous species
Bibliographic record
Abstract
The main objective of evidence-based management is to promote use of scientific data in the decision-making process of managers, with data either complementing or replacing expert knowledge. It is expected that this will increase the efficiency of environmental interventions. However, the relative accuracy and precision of evidence-based tools and expert knowledge has seldom been evaluated. It is therefore essential to verify whether such tools provide better decision support before advocating their use. We conducted an elicitation survey in which experts were asked to (1) evaluate the influence of various factors on the success of eradication programs for aquatic nonindigenous species and (2) provide probabilities of success for real case studies for which we knew the outcome. The responses of experts were compared with the results and predictions of a newly developed evidence-based tool: a statistical model calibrated with a meta-analysis of case studies designed to evaluate probability of eradication. Experts and the model generally identified the same factors as influencing the probability of success. However, the model provided much more accurate estimates for the probability of eradication than expert opinion, strongly suggesting that an evidence-based approach is superior to expert knowledge in this case. Uncertainty surrounding the predictions of the evidence-based tool was similar to among-expert variability. Finally, a model based on >30 case studies returned more accurate predictions than expert opinion. We conclude that decision-making processes based on expert judgment would greatly benefit from incorporating evidence-based tools.
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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 | 0.187 | 0.478 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.006 |
| Bibliometrics | 0.014 | 0.007 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.006 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".