Bibliographic record
Abstract
Less is more 30Rice farmers in Hubei Province discover, much to their surprise, that a new method of irrigation called alternate wetting and drying saves them water, labor, time and money, while boosting crop yield and profitability Material improvement: 35 Conserving and using crop and livestock genetic resources After the flood 36As burgeoning demand for water strains supplies across China, especially in the north, rice scientists work with farmers to refine aerobic rice, an emerging technology for continued bountiful rice harvests from dry land Liang Guangrun grows sweetpotato and maize for fattening pigs for market, as well as peanuts and rice for home consumption.Li Zhenghong, technical advisor for China's only pigeonpea association, holds an immature pod of tamarind, which farmers often interplant with pigeonpea. ICRISAT is the CGIAR Center in this research partnership.Peanut farmer Yu Meilian and her husband, Zheng Dechao, credit training from agricultural extensionists and improved varieties for their improved self-sufficiency.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.010 | 0.005 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.023 | 0.002 |
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".