A generalized framework for AMOVA with multiple hierarchies and ploidies
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
The analysis of molecular variance (AMOVA) is a widely used statistical method in population genetics and molecular ecology. The classic framework of AMOVA only supports haploid and diploid data, in which the number of hierarchies ranges from two to four. In practice, natural populations can be classified into more hierarchies, and polyploidy is frequently observed in extant species. The ploidy level may even vary within the same species, and/or within the same individual. We generalized the framework of AMOVA such that it can be used for any number of hierarchies and any level of ploidy. Based on this framework, we present four methods to account for data that are multilocus genotypic and allelic phenotypic (with unknown allele dosage). We use simulated datasets and an empirical dataset to evaluate the performance of our framework. We make freely available our methods in a new software package, polygene, which is freely available at https://github.com/huangkang1987/polygene.
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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