Reliable Identification of Declining Populations in an Uncertain World
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it