{"id":"W4386458681","doi":"10.4038/jas.v18i3.9705","title":"Effect of Different Nutrient Management Systems on Yield and Yield Components of Rice Crop (&lt;em&gt;Oryza sativa&lt;/em&gt; L.) in the Dry Zone of Sri Lanka","year":2023,"lang":"en","type":"article","venue":"Journal of Agricultural Sciences – Sri Lanka","topic":"Rice Cultivation and Yield Improvement","field":"Agricultural and Biological Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Panicle; Nutrient management; Cropping system; Nutrient; Agronomy; System of Rice Intensification; Yield (engineering); Dry season; Wet season; Oryza sativa; Cropping; Environmental science; Crop; Crop yield; Mathematics; Biology; Agriculture; Ecology","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001806143,0.0002411511,0.0005565991,0.0001022748,0.0001718987,0.00008958771,0.0006675873,0.00008962462,0.00003427815],"category_scores_gemma":[0.0001530735,0.0000679465,0.0001871094,0.001201494,0.000154494,0.0001981815,0.0001394816,0.0002009266,0.000003797999],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004438727,"about_ca_system_score_gemma":0.000006460536,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002159963,"about_ca_topic_score_gemma":0.0001502125,"domain_scores_codex":[0.9970354,0.000274127,0.0008719012,0.0002678379,0.001217923,0.0003327668],"domain_scores_gemma":[0.9976283,0.001106263,0.0009154719,0.00008611339,0.000162702,0.0001011657],"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":[0.0001898578,0.0004957807,0.01036658,0.0003070348,0.00009049444,0.00001522367,0.004680292,0.0002216318,0.9757404,0.0005952672,0.001150202,0.006147238],"study_design_scores_gemma":[0.0006673827,0.003826372,0.9473719,0.0008666585,0.00005933151,0.0000164506,0.01686489,0.00007379394,0.02940939,0.0000280334,0.0006017871,0.0002140023],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.996867,0.0002400868,0.000001396675,0.0009363683,0.0003519888,0.0005601666,0.00002134215,0.00001175715,0.001009899],"genre_scores_gemma":[0.9993443,0.0003222352,0.00001506666,0.00007386103,0.0001230136,0.00001216285,0.00001007347,8.005538e-7,0.00009851992],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.946331,"threshold_uncertainty_score":0.2770778,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03031487141474804,"score_gpt":0.2400041844301827,"score_spread":0.2096893130154347,"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."}}