Effect of ecological uncertainty on species at risk decision-making: COSEWIC expert opinion as a case study
Bibliographic record
Abstract
Uncertainty about ecological variables can affect risk designations for species at risk of extinction. This study evaluated the effect of quantitatively characterizing ecological uncertainty on species at risk decision-making, using the Committee on the Status of Endangered Wildlife in Canada (COSEWIC) as a case study. Fifty senior authors of COSEWIC assessments of vertebrate species were invited to use a confidential web-based survey to quantitatively characterize uncertainty in their expert opinions for 17 COSEWIC ecological variables. Probability distributions for the 17 variables provided by each of 16/50 (32.0%) respondents were used with Monte Carlo sampling to generate sets of point estimates used as input for a computer algorithm that emulated COSEWIC decision-making for risk designation. The effect of uncertainty on risk designation was measured as a Monte Carlo-generated probability for the same risk designation as that determined by the mean point estimates only. Analysis of uncertainty revealed plausible alternative designations for seven of the 16 species. Although the majority of these cases were affected in a relatively minor way, there were cases where the explicit characterization of uncertainty caused major differences in risk designation. From these results, it can be concluded that characterization of uncertainty can have important effects on species at risk decision-making. Responsible agencies should explicitly incorporate uncertainty in their decision-making by (1) requiring explicit characterization of uncertainty for input ecological variables and output risk designations and (2) developing rational methods to incorporate the uncertainty in decision-making.
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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 | 0.059 | 0.140 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".