Selection criteria for scoring amplified fragment length polymorphisms (AFLPs) positively affect the reliability of population genetic parameter estimates
Bibliographic record
Abstract
A reliable data set is a fundamental prerequisite for consistent results and conclusions in population genetic studies. However, marker scoring of genetic fingerprints such as amplified fragment length polymorphisms (AFLPs) is a highly subjective procedure, inducing inconsistencies owing to personal or laboratory-specific criteria. We applied two alternative marker selection algorithms, the newly developed script scanAFLP and the recently published AFLPScore, to a large AFLP genome scan to test how population genetic parameters and error rates were affected. These results were confronted with replicated random selections of marker subsets. We show that the newly developed marker selection criteria reduced the mismatch error rate and had a notable influence on estimates of genetic diversity and differentiation. Both effects are likely to influence biological inference. For example, genetic diversity (HS) was 29% lower while genetic differentiation (FST) was 8% higher when applying scanAFLP compared with AFLPScore. Likewise, random selections of markers resulted in substantial deviations of population genetic parameters compared with the data sets including specific selection criteria. These randomly selected marker sets showed surprisingly low variance among replicates. We conclude that stringent marker selection and phenotype calling reduces noise in the data set while retaining patterns of population genetic structure.
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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 | 0.000 | 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 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".