{"id":"W2943178178","doi":"","title":"Stabilizing yield and quality: early maturing chickpea for the prairies","year":2006,"lang":"en","type":"article","venue":"","topic":"Genetic and Environmental Crop Studies","field":"Agricultural and Biological Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Yield (engineering); Quality (philosophy); Agronomy; Environmental science; Biology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004668363,0.0002435512,0.0001202791,0.0001865007,0.0004706728,0.0004761746,0.0003960178,0.0001133481,0.001429091],"category_scores_gemma":[0.0003906171,0.00009822596,0.00008886034,0.0001979361,0.0001599502,0.0002979542,0.0003083825,0.0002941753,0.0001774608],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001247744,"about_ca_system_score_gemma":0.001088887,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.05851745,"about_ca_topic_score_gemma":0.2694217,"domain_scores_codex":[0.9998982,0.00001531552,0.00000488561,0.00003400794,0.0000324185,0.00001518833],"domain_scores_gemma":[0.9997049,0.00002248895,0.00008244155,0.00003488086,0.0000671462,0.00008816307],"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":[0.000831191,0.0002833868,0.09298261,0.0001652355,0.00008778436,0.0003467384,0.0005989543,0.001296854,0.8418071,0.001278541,0.0005253352,0.05979633],"study_design_scores_gemma":[0.00004715986,0.0006184181,0.9728319,0.00001889418,0.00005478763,0.0003015634,0.0002125847,0.001331577,0.01974895,0.0001993228,0.004621688,0.00001316074],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9975068,0.000354007,0.0009048455,0.00009694137,0.000004325415,0.00001232615,0.00009764374,0.00002116668,0.001001911],"genre_scores_gemma":[0.992831,0.0002474166,0.00426161,0.00003466376,0.000003572059,0.000008206398,0.000189278,0.00001632076,0.002407873],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05851745,"threshold_uncertainty_score":0.1163537,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04424431246806108,"score_gpt":0.2173117350709892,"score_spread":0.1730674226029282,"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."}}