AveDissR: An R function for assessing genetic distinctness and genetic redundancy
Bibliographic record
Abstract
PREMISE OF THE STUDY: Assessing genetic distinctness or redundancy is an important part of plant germplasm characterization. We previously introduced a new marker-based approach using the average dissimilarity of an accession to assess genetic distinctness or redundancy. However, this approach has not been widely applied, largely due to the lack of software to integrate separate analyses involving dissimilarity calculation, analysis of molecular variance, and principal coordinates analysis. METHODS AND RESULTS: An R function, AveDissR, was developed to integrate three separate analyses into one package for assessing genetic distinctness or redundancy. It can analyze large data sets of dominant or codominant markers such as amplified fragment length polymorphisms (AFLPs), simple sequence repeats (SSRs), or single-nucleotide polymorphisms (SNPs), generate a useful set of output files for germplasm assessment, and run in an R environment on any computer platform. CONCLUSIONS: AveDissR can make the assessment of genetic distinctness or redundancy in plant germplasm more feasible and useful.
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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 | 0.007 | 0.023 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.023 | 0.022 |
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".