Búsqueda de genes candidatos asociados a caracteres de interés \nagronómico en garbanzo (Cicer arietinum L.)
Bibliographic record
Abstract
Las leguminosas son una fuente importante de proteínas y carbohidratos, además tienen \ncapacidad para establecer simbiosis con la bacteria Rhizobium fijando el nitrógeno \natmosférico en el suelo, por lo tanto es muy importante integrar estos cultivos en los \nsistemas de rotación para enriquecer el suelo con nitrógeno de manera natural (Aslam et \nal 2003). El garbanzo es la segunda leguminosa grano más importante del mundo \ndespués de las judías (Faostat, 2013). Sin embargo todavía no hay estudios relevantes a \nnivel molecular para entender los mecanismos de adaptación como el hábito de \ncrecimiento, simple/doble vaina y la fecha de floración siendo la mayoría de los estudios \npublicados de genética clásica (Mathews and Davis 1999; Rajesh et al. 2002; Aryamanesh \net al. 2013; Gaur et al. 2014). Estos caracteres son críticos para incrementar el \nrendimiento del cultivo (Rubio et al. 2004; Gaur et al. 2008). La secuenciación del genoma \nde garbanzo recién publicado (Jain et al. 2013; Varshney et al. 2013) ha sido de gran \nutilidad para buscar nuevos marcadores aplicables en MAS (marker assisted selection) y \ngenes candidatos. No obstante, disponer de poblaciones de mapeo como RIPs \n(recombinant inbred populations) y líneas casi isogénicas (NILs) que estén \ncuidadosamente fenotipadas, son fundamentales para llevar a cabo estos estudios.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".