Bibliographic record
Abstract
One of the best methods to manage BPH is to utilize resistant varieties. Ever since BPH started appearing in epidemic proportions since early nineteen hundred seventies, International Rice Research Institute based at Los Banās, Philippines (IRRI) and many national programs in Asian countries have started developing BPH resistant varieties suitable for their conditions. To date, 34 loci each probably having many genes have been identified to contribute for resistance reaction in rice against BPH and mapped to seven of the 12 chromosomes (1, 2, 3, 4, 6, 11, and 12) of rice. Apart from few hundred land races of cultivated rice O . sativa , several wild rices like Oryza rufipogon (AA genome), O . officinalis (CC genome), Oryza eichingeri (CC genome), O . minuta (BBCC genome), O. latifolia (CCDD genome), O . australiensis (EE genome), O . punctata (BB and BBCC genome) and O. granulate (GG genome) served as sources of resistance. Some of the latest genes like Bph31, Bph32 and Bph34 can hopefully serve as excellent sources of resistance in containing devastating BPH populations in several rice growing countries in Asia. However, there is lot of confusion in naming different biotypes of BPH. There is equally confusion in naming rice genes that confer resistance to different BPH biotypes. An International Committee preferably under the auspices of IRRI can be constituted to sort out the differences and stream line the whole information.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".