<i>parallelnewhybrid</i>: an R package for the parallelization of hybrid detection using <scp>newhybrids</scp>
Bibliographic record
Abstract
Abstract Hybridization among populations and species is a central theme in many areas of biology, and the study of hybridization has direct applicability to testing hypotheses about evolution, speciation and genetic recombination, as well as having conservation, legal and regulatory implications. Yet, despite being a topic of considerable interest, the identification of hybrid individuals, and quantification of the (un)certainty surrounding the identifications, remains difficult. Unlike other programs that exist to identify hybrids based on genotypic information, newhybrids is able to assign individuals to specific hybrid classes (e.g. F1, F2) because it makes use of patterns of gene inheritance within each locus, rather than just the proportions of gene inheritance within each individual. For each comparison and set of markers, multiple independent runs of each data set should be used to develop an estimate of the hybrid class assignment accuracy. The necessity of analysing multiple simulated data sets, constructed from large genomewide data sets, presents significant computational challenges. To address these challenges, we present parallelnewhybrid, an r package designed to decrease user burden when undertaking multiple newhybrids analyses. parallelnewhybrid does so by taking advantage of the parallel computational capabilities inherent in modern computers to efficiently and automatically execute separate newhybrids runs in parallel. We show that parallelization of analyses using this package affords users several‐fold reductions in time over a traditional serial analysis. parallelnewhybrid consists of an example data set, a readme and three operating system‐specific functions to execute parallel newhybrids analyses on each of a computer's c cores. parallelnewhybrid is freely available on the long‐term software hosting site github ( www.github.com/bwringe/parallelnewhybrid ).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 | 0.026 |
| Meta-epidemiology (narrow) | 0.005 | 0.004 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.007 | 0.006 |
| Research integrity | 0.002 | 0.007 |
| Insufficient payload (model declined to judge) | 0.063 | 0.041 |
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".