Reliable Identification of Declining Populations in an Uncertain World
Bibliographic record
Abstract
Abstract Assessments of extinction risk based on population declines are widely used, yet scientists have little quantitative understanding of their reliability. Incorrectly classifying whether a population is declining or not can lead to inappropriate conservation actions or management measures, with potentially profound societal costs. Here we evaluate key causes of misclassification of decline status and assess the reliability of 20 decline metrics using a stochastic model to simulate time series of population abundance of sockeye salmon ( Oncorhynchus nerka ). We show that between‐year variability in population productivity (process variation) and, to a lesser extent, variability in abundance estimates (observation error) are important causes of unreliable identification of population status. We found that using all available data, rather than just the most recent three generations, consistently improved the reliability of risk assessments. The approach outlined here can improve understanding of the reliability of risk assessments, thereby reducing concerns that may impede their use for exploited taxa such as marine fishes.
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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 | 0.007 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".