Applying expert elicitation of viability and persistence to a lynx species status assessment
Bibliographic record
Abstract
Abstract In 2015, the United States Fish and Wildlife Service initiated a review of the status of Canada lynx ( Lynx canadensis ) in the contiguous United States. Available research and monitoring, while substantial, lacked information on the demographic rates, abundance, and trends necessary to complete a full viability assessment. Therefore, alternative sources of information were needed to inform the species status assessment. We designed and conducted an expert elicitation to capture the knowledge, professional judgments, and opinions of lynx experts to assess the status of, and the drivers influencing, these lynx populations. We elicited the likelihood and level of uncertainty regarding future persistence over several time frames (at years 2025, 2050, and 2100). The elicitation revealed experts' concerns that expected climate‐driven losses in habitat quality, quantity, and related factors will likely result in declines. Experts expect resident populations of lynx will persist in all five currently occupied geographic units in 2025; in 4 or 5 of the units at 2050; and in 2 or 3 units at 2100. Experts expressed a high level of uncertainty regarding the rate and extent of decline due to projected climate warming and corresponding effects to these lynx populations. In the absence of adequate monitoring data, this type of expert elicitation is a useful method to aid classification decisions, such as providing the scientific information the Service relied upon to complete the November 5, 2017 5‐year review which recommended that the lynx distinct population segment be removed from the list of threatened and endangered species.
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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 | 0.083 | 0.199 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".