Guidelines for MSAT and SNP panels that lead to high-quality data for genetic mark–recapture studies
Bibliographic record
Abstract
Molecular markers with inadequate power to discriminate among individuals can lead to false recaptures (shadows), and inaccurate genotyping can lead to missed recaptures (ghosts), potentially biasing genetic mark–recapture estimates. We used simulations to examine the impact of microsatellite (MSAT) and single nucleotide polymorphism (SNP) marker-set size, allelic frequency, multitubes approaches, and sample matching protocols on shadow and ghost events in genetic mark–recapture studies, presenting guidance on the specifications for MSAT and SNP marker panels, and sample matching protocols necessary to produce high-quality data. Shadow events are controllable by increasing the number of markers or by selecting markers with high discriminatory power; reasonably sized marker sets (e.g., ≥9 MSATs or ≥32 SNPs) of moderate allelic diversity lead to low probabilities of shadow errors. Ghost events are more challenging to control and low allelic dropout or false-allele error rates produced high rates of erroneous mismatches in mark–recapture sampling. Fortunately, error-tolerant matching protocols, which use information from positively matching loci between comparisons of samples, and multitubes protocols to achieve consensus genotypes are effective at eliminating ghost events. We present a case study on Pacific walrus, Odobenus rosmarus divergens (Illiger, 1815), using simulation results to inform genetic marker choices.
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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 | 0.055 | 0.131 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.006 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.014 | 0.013 |
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".