Bibliographic record
Abstract
ABSTRACT This is a reference manual for the elegant, yet hideously complex concept of effective population size ( N e ), inspired by a classic, self‐published manual of automotive repair ‘for the compleat idiot’. The Guide is timely, given the recent Kunming‐Montreal Global Biodiversity Framework, where 196 Parties committed to tracking genetic diversity—and estimating N e —for all species. N e is a human construct, but a useful one that allows us to capture diverse aspects of an organism's biology in a single number. The Guide collates in one location factual information about effective population size, with a focus on topics of practical relevance to scientists and managers studying real populations; it covers definition, computation and estimation of effective size, both demographically and genetically. As appropriate, the reader is directed to other primary sources for more details. A ‘Don't Do These Things’ section lists several ill‐advised approaches to dealing with N e , and an Appendix provides useful tools and practical suggestions for interested users. A special section considers both possibilities and challenges presented by the genomics revolution. Availability of vast numbers of genetic markers increases precision, but less than some might think, and simultaneously introduces new challenges involving filtering and bioinformatics processing. As annotated genomes become more common for non‐model species, opportunities are opened to address qualitatively different questions, including reconstructing historical changes in N e through time.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.035 | 0.025 |
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".