<scp>hybriddetective</scp>: A workflow and package to facilitate the detection of hybridization using genomic data in <scp>r</scp>
Bibliographic record
Abstract
The ability to detect and characterize hybridization in nature has long been of interest to many fields of biology and often has direct implications for wildlife management and conservation. The capacity to identify the presence of hybridization, and quantify the numbers of individuals belonging to different hybrid classes, permits inference on the magnitude of, and timescale over which, hybridization has been or is occurring. Here, we present an r package and associated workflow developed for the detection, with estimates of efficiency and accuracy, of multigenerational hybrid individuals using genetic or genomic data in conjunction with the program newhybrids. This package includes functions for the identification and testing of diagnostic panels of markers, the simulation of multigenerational hybrids, and the quantification and visualization of the efficiency and accuracy with which hybrids can be detected. Overall, this package delivers a streamlined hybrid analysis platform, providing improvements in speed, ease of use and repeatability over current ad hoc approaches. The latest version of the package and associated documentation are available on GitHub (https://github.com/bwringe/hybriddetective).
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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 | 0.020 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.104 | 0.082 |
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".