Estimating effective population size using close‐kin mark–recapture
Bibliographic record
Abstract
Abstract Close‐kin mark–recapture (CKMR) is a method that allows estimating population census size, among other parameters, through the observed number of pairs that are close‐kin including parent–offspring pairs (POPs) and half‐sibling pairs (HSPs). CKMR models are capable of estimating abundance, fecundity and survival at age using POPs and HSPs from different cohorts. The link between effective population size ( N e ) and the number of ‘within‐cohort’ sibling pairs has been noted before but how to actually achieve such an estimate with CKMR has not been previously demonstrated. We show it is possible to use the number of ‘within‐cohort’ sibling pairs along with POPs and ‘different‐cohort’ HSPs to estimate N e . These can be combined with an estimate of annual variance in number of offspring to estimate the lifetime variance of total reproductive success which can be used to find N e . We show that the variance in number of offspring produced by adults in a given year is related to the within‐cohort comparisons. Our approach is demonstrated on an individual‐based simulation where we show that the CKMR N e estimate offers similar results to N e estimated from methods like linkage disequilibrium. Our methods allow estimating N e using CKMR while also estimating demographic parameters.
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How this classification was reachedexpand
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 | 0.002 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".