Bibliographic record
Abstract
Insertional mutant databases containing Flanking Sequence Tags (FSTs) are becoming key resources for plant functional genomics. We have developed OryGenesDB (http://orygenesdb.cirad.fr/), a database dedicated to rice reverse genetics. Insertion mutants of rice genes are catalogued by Flanking Sequence Tag (FST) information that can be readily accessed by this database. Our database presently contains 44166 FSTs generated by most of the rice insertional mutagenesis projects. The OryGenesDB genome browser is based on the powerful Generic Genome Browser (GGB) developed in the framework of the Generic Model Organism Project (GMOD). The main interface of our web site displays search and analysis interfaces to look for insertions in any candidate gene of interest. Several starting points can be used to exhaustively retrieve the insertions positions and associated genomic information using blast, keywords or gene name search. The toolbox integrated in our database also includes an 'anchoring' option that allows immediate mapping and visualization of up to 50 nucleic acid sequences in the rice Genome Browser of OryGenesDB. As a first step toward plant comparative genomics, we have linked the rice and Arabidopsis whole genome using all the predicted pairs of orthologs by best BLAST mutual hit (BBMH) connectors.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.027 | 0.047 |
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".