{"id":"W2134224844","doi":"","title":"Application of supervised feature selection methods to define the most important traits affecting maximum kernel water content in maize","year":2011,"lang":"en","type":"article","venue":"Adelaide Research & Scholarship (AR&S) (University of Adelaide)","topic":"Genetics and Plant Breeding","field":"Agricultural and Biological Sciences","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Shiraz University; Universidad Nacional de Rosario","keywords":"Kernel (algebra); Sowing; Agronomy; Selection (genetic algorithm); Mathematics; Feature selection; Water content; Agriculture; Biology; Machine learning; Computer science; Engineering; Ecology","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":[0.001098242,0.0007258588,0.0006125301,0.001052798,0.0002173676,0.000362686,0.0003446409,0.0002886359,0.0003465099],"category_scores_gemma":[0.002059643,0.0001063177,0.000744401,0.0006266724,0.0001524437,0.0002640414,0.0002416739,0.0002719674,0.0001321395],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000239835,"about_ca_system_score_gemma":0.0005217477,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002851532,"about_ca_topic_score_gemma":0.002171201,"domain_scores_codex":[0.9995191,0.0001783741,0.00004392013,0.0001129361,0.0000880159,0.00005752182],"domain_scores_gemma":[0.99898,0.0005146883,0.0001187951,0.00005874626,0.0002959047,0.00003177948],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001023011,0.0008249091,0.06846965,0.0002645781,0.000494802,0.0003096225,0.0003072865,0.1874305,0.07489395,0.0005790711,0.002192552,0.6632101],"study_design_scores_gemma":[0.00005628942,0.0003528915,0.06537626,0.00001539315,0.0001264481,0.0001295858,0.0001487249,0.9140903,0.01788356,0.0008444274,0.0009343623,0.00004173634],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.740445,0.000360339,0.2566155,0.0001004985,0.00003780214,0.0001174171,0.0007167325,0.0008367538,0.0007699951],"genre_scores_gemma":[0.9322887,0.00006126284,0.06580704,0.00002376666,0.00002130305,0.0001198151,0.001197529,0.00003373886,0.0004467993],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.002851532,"threshold_uncertainty_score":0.005808175,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1476559289830667,"score_gpt":0.2987252743298883,"score_spread":0.1510693453468216,"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."}}