STORM: software for testing hypotheses of relatedness and mating patterns
Bibliographic record
Abstract
Storm is a software package that allows users to test a variety of hypotheses regarding patterns of relatedness and patterns of mate choice and/or mate compatibility within a population. These functions are based on four main calculations that can be conducted either independently or in the hypothesis-testing framework: internal relatedness; homozygosity by loci; pairwise relatedness; and a new metric called allele inheritance, which calculates the proportion of loci at which an offspring inherits a paternal allele different from that inherited from its mother. STORM allows users to test four hypotheses based on these calculations and Monte Carlo simulations: (i) are individuals within observed associations or groupings more/less related than expected; (ii) do observed offspring have more/less genetic variability (based on internal relatedness or homozygosity by loci) than expected from the gene pool; (iii) are observed mating pairs more/less related than expected if mating is random with respect to relatedness; and (iv) do observed offspring inherit paternal alleles different from those inherited from the mother more/less often than expected based on Mendelian inheritance.
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 | 0.008 | 0.018 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.091 | 0.026 |
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".