{"id":"W2009063797","doi":"10.5539/jas.v2n2p164","title":"Wheat Production in India: Technologies to Face Future Challenges","year":2010,"lang":"en","type":"article","venue":"Journal of Agricultural Science","topic":"Wheat and Barley Genetics and Pathology","field":"Agricultural and Biological Sciences","cited_by":41,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Green Revolution; Agriculture; Production (economics); Investment (military); Selection (genetic algorithm); Scale (ratio); Task (project management); Natural resource economics; Yield (engineering); Business; Natural resource; Emerging technologies; Face (sociological concept); Agroforestry; Biotechnology; Agricultural economics; Geography; Engineering; Computer science; Economics; Biology; Ecology; Political science","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006513397,0.0001100664,0.0001679172,0.00006125725,0.0001573795,0.00006464712,0.0005920128,0.0001158022,0.00001534311],"category_scores_gemma":[0.0001409062,0.00003151432,0.0000544638,0.001020848,0.0001485671,0.0002957953,0.00009246001,0.0003760504,0.000008989944],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002334174,"about_ca_system_score_gemma":0.00001768346,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006982485,"about_ca_topic_score_gemma":0.0004361811,"domain_scores_codex":[0.9988776,0.00002075698,0.0002454397,0.0002275602,0.0003312891,0.0002973981],"domain_scores_gemma":[0.9994643,0.00002561309,0.0001246455,0.00004957617,0.0002337604,0.0001020986],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.000006436493,0.0000371214,0.00141173,0.000001396244,9.544807e-7,0.000004925896,0.0002436834,0.000006413291,0.8943686,0.0001987028,0.000220733,0.1034993],"study_design_scores_gemma":[0.00004722241,0.0003544341,0.8876719,0.0000159484,0.000002618727,0.0002512475,0.003662218,3.919822e-7,0.1041479,0.0002707599,0.003465962,0.0001093573],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9640869,0.000531623,1.665423e-7,0.03355711,0.001284045,0.0001155997,0.000001540633,0.00001997633,0.000402999],"genre_scores_gemma":[0.9981743,0.0005486381,0.0003915325,0.00004579917,0.000786648,0.000002755178,5.658553e-7,3.287915e-7,0.00004940594],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8862602,"threshold_uncertainty_score":0.1633773,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01337995585162895,"score_gpt":0.2230337643882801,"score_spread":0.2096538085366512,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}