Bibliographic record
Abstract
Sorghum (Sorghum bicolor [L.] Moench) is the fifth \nmost important cereal crop by area after wheat, rice, \nmaize and barley in the world. Traditionally, a staple \nfood crop for millions of poor in the semi-arid tropics \n(SAT) of Africa and Asia, its importance as a fodder \nand feed crop for livestock steadily increased over \nthe last decade or two. It is cultivated on marginal, \nfragile drought-prone environments in SAT. In mid- \n1970s, the productivity levels of sorghum were <0.7 \nt ha-1 in Africa, <0.8 t ha-1 in Asia and <0.5 t ha-1 in \nIndia when the International Crops Research \nInstitute for the Semi-Arid Tropics (ICRISAT) was \nestablished at Patancheru, Andhra Pradesh, India. \nLow productivity was the result of dependency on \ntraditional cultivars and management practices and \nexacerbated by an array of biotic stresses (insect \npests—shoot fly, stem borers, midge and headbugs; \ndiseases—grain mold, anthracnose, rust, downy \nmildew, leaf blight and Striga); and abiotic stresses \n(drought and problematic soils—acidic and saline). \nICRISAT, in close collaboration with the National \nAgricultural Research Systems (NARS) in SAT, the \nAdvanced Research Institutes (ARIs) and sister \norganizations of the Consultative Group on \nInternational Agricultural Research (CGIAR) over \nthe years has been engaged in improving the \nproductivity of sorghum in various SAT regions \nthrough genetic improvement of sorghum coupled \nwith integrated genetic and natural resources \nmanagement approaches. These research efforts \nhave led to many and diversified impacts; overall \neffect being the significantly improved livelihoods \nof resource-poor farmers and low-income people \nin SAT.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.009 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".