Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Genetic interaction studies have been used to characterize unknown genes, assign membership in pathway and complex, and build a comprehensive functional map of a eukaryotic cell. Synthetic genetic array (SGA) methodology automates yeast genetic analysis and enables systematic mapping of genetic interactions. In its simplest form, SGA consists of a series of replica pinning steps that enable construction of haploid double mutants through automated mating and meiotic recombination. Using this method, a strain carrying a query mutation, such as a deletion allele of a nonessential gene or a conditional temperature-sensitive allele of an essential gene, can be crossed to an input array of yeast mutants, such as the complete set of approximately 5000 viable deletion mutants. The resulting output array of double mutants can be scored for genetic interactions based on estimates of cellular fitness derived from colony-size measurements. The SGA score method can be used to analyze large-scale data sets, whereas small-scale data sets can be analyzed using SGAtools, a simple web-based interface that includes all the necessary analysis steps for quantifying genetic interactions.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it