Effective/census population size ratio estimation: a compendium and appraisal
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
With an ecological-evolutionary perspective increasingly applied toward the conservation and management of endangered or exploited species, the genetic estimation of effective population size (N(e)) has proliferated. Based on a comprehensive analysis of empirical literature from the past two decades, we asked: (i) how often do studies link N(e) to the adult census population size (N)? (ii) To what extent is N(e) correctly linked to N? (iii) How readily is uncertainty accounted for in both N(e) and N when quantifying N(e)/N ratios? and (iv) how frequently and to what degree might errors in the estimation of N(e) or N affect inferences of N(e)/N ratios? We found that only 20% of available N(e) estimates (508 of 2617; 233 studies) explicitly attempted to link N(e) and N; of these, only 31% (160 of 508) correctly linked N(e) and N. Moreover, only 7% (41 of 508) of N(e)/N ratios (correctly linked or not) reported confidence intervals for both N(e) and N; for those cases where confidence intervals were reported for N(e) only, 31% of N(e)/N ratios overlapped with 1, of which more than half also reached below N(e)/N = 0.01. Uncertainty in N(e)/N ratios thus sometimes spanned at least two orders of magnitude. We conclude that the estimation of N(e)/N ratios in natural populations could be significantly improved, discuss several options for doing so, and briefly outline some future research directions.
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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