{"id":"W4386458681","doi":"10.4038/jas.v18i3.9705","title":"Effect of Different Nutrient Management Systems on Yield and Yield Components of Rice Crop (<em>Oryza sativa</em> 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002905166,0.000345228,0.0002854485,0.0002660273,0.0003610444,0.0005327224,0.0003478931,0.0001499918,0.0004568297],"category_scores_gemma":[0.000320531,0.0001250854,0.0003019964,0.0003314397,0.0003442212,0.0003381862,0.0003974625,0.0002820572,0.00008094317],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00116987,"about_ca_system_score_gemma":0.0008930733,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01284552,"about_ca_topic_score_gemma":0.03854677,"domain_scores_codex":[0.9997498,0.0000425095,0.00003000053,0.00008521727,0.00004020652,0.00005220596],"domain_scores_gemma":[0.9996384,0.00007032636,0.0001273941,0.00001868134,0.00005635624,0.00008881272],"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":[0.005853256,0.001187115,0.3997655,0.0007896826,0.0006186246,0.0007292599,0.001207407,0.001603859,0.5410304,0.0001814095,0.0003856642,0.04664782],"study_design_scores_gemma":[0.00003412341,0.003819917,0.9777199,0.0000124099,0.0001375832,0.00009050858,0.0008572119,0.0007957402,0.01581905,0.00004606254,0.0006490039,0.00001849701],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.999661,0.00007948616,0.00005303713,0.000006761486,0.000001621074,0.000005834394,0.0000520866,0.000003322245,0.0001368094],"genre_scores_gemma":[0.9989202,0.0001087418,0.0004127627,0.00002242133,0.000001955727,0.00002374694,0.0002199198,0.000002993962,0.0002873998],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01284552,"threshold_uncertainty_score":0.02554148,"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."}}