Imperfect detection biases extinction‐debt assessments
Bibliographic record
Abstract
Abstract Freshwater ecosystems have been substantially altered, threatening the survival and recovery of aquatic species at risk. Estimating the likelihood and magnitude of future extinctions (extinction debt; ED) is integral for conserving biodiversity and requires accurate species composition lists. Using species‐area relationships, we estimated ED for fishes in historically disturbed wetlands in the Lake Erie basin. Then, we used simulated data sets to assess how ED varied when species lists used to derive species‐area relationships had an increasing proportion of undetected species. When species lists were incomplete, ranging from 0.99 to 0.75, 15% fewer wetlands were estimated to have species in ED and, on average, 50% fewer species were expected to go extinct per wetland. Imperfect detection ultimately biased conservation prioritization among wetlands. Our findings suggest that if imperfect detection is not accounted for when projecting future extinctions, the severity of future species loss across a landscape, and the subsequent need for immediate restorative action, can be greatly underestimated.
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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 | 0.019 | 0.097 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".