related: an R package for analysing pairwise relatedness from codominant molecular markers
Bibliographic record
Abstract
Analyses of pairwise relatedness represent a key component to addressing many topics in biology. However, such analyses have been limited because most available programs provide a means to estimate relatedness based on only a single estimator, making comparison across estimators difficult. Second, all programs to date have been platform specific, working only on a specific operating system. This has the undesirable outcome of making choice of relatedness estimator limited by operating system preference, rather than being based on scientific rationale. Here, we present a new R package, called related, that can calculate relatedness based on seven estimators, can account for genotyping errors, missing data and inbreeding, and can estimate 95% confidence intervals. Moreover, simulation functions are provided that allow for easy comparison of the performance of different estimators and for analyses of how much resolution to expect from a given data set. Because this package works in R, it is platform independent. Combined, this functionality should allow for more appropriate analyses and interpretation of pairwise relatedness and will also allow for the integration of relatedness data into larger R workflows.
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 | 0.003 | 0.022 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.056 | 0.049 |
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".