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
Abstract The goal of the Biological General Repository for Interaction Datasets (BioGRID) (http://www.thebiogrid.org) is to archive and freely disseminate collections of genetic and protein interactions from major model organisms. BioGRID currently houses over 335,000 interactions curated from high-throughput datasets and individual focused studies found in the primary literature, as derived from some 23,000 publications. Complete coverage of the entire literature for both the budding yeast Saccharomyces cerevisiae and the fission yeast Schizosaccharomyces pombe has been achieved, resulting in the curation of over 246,000 interactions, and efforts to expand curation across multiple species are underway. Through collaborations with the Gene Ontology (GO) Consortium and the Linking Animal Models to Human Disease Initiative (LAMHDI), we are focusing our curation efforts across model organisms on particular areas of biology to enable insights into conserved networks and pathways that are relevant to human health.The BioGRID 3.0 web interface contains new search and display features that enable rapid queries across multiple data types and sources. A dedicated Interaction Management System (IMS) is used to track all curation and to prioritize publications across multiple curation projects. BioGRID data are incorporated in several model organism databases and other biological databases. The entire BioGRID interaction collection may be downloaded in multiple file formats, including PSI MI XML, and source code for BioGRID is freely available without any restrictions. This work is supported by NIH NCRR grant R01 RR024031 to MT and KD, and by grants from the CIHR and BBSRC to MT.
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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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