<scp>genepopedit</scp>: a simple and flexible tool for manipulating multilocus molecular data in R
Bibliographic record
Abstract
Advances in genetic sequencing technologies and techniques have made large, genome-wide data sets comprised of hundreds or even thousands of individuals and loci the norm rather than the exception even for nonmodel organisms. While such data present new opportunities for evaluating population structure and demographic processes, the large size of these genomic data sets brings new computational challenges for researchers needing to parse, convert and manipulate data often into a variety of software-specific formats required of genomic analyses. We developed genepopedit as a flexible tool for the manipulation of multilocus molecular data sets. Functionality can be divided among diagnostic-, manipulation-, sampling-, simulation-, and transformation-based tools. Metadata from large genomic data sets can be efficiently extracted, without the need to view data in a text-editing program. genepopedit provides tools to manipulate loci, individual samples and populations included in genomic data sets, in addition to the ability to convert directly to a variety of software formats. Functions are compiled as an R package, which can integrate into existing analysis workflows. Importantly, genepopedit provides a simple yet robust code-based tool for repeatable genomic data manipulation, which has been proven to be stable for data sets in excess of 200 000 SNPs. The latest version of the package and associated documentation are available on Github (github.com/rystanley/genepopedit).
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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 | 0.005 | 0.022 |
| Meta-epidemiology (narrow) | 0.005 | 0.004 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.008 | 0.005 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.177 | 0.138 |
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".