{"id":"W6947760672","doi":"10.4224/21263082","title":"A direct polynomial regression for PMM data analysis and application","year":2012,"lang":"en","type":"report","venue":"NPARC","topic":"Chemical synthesis and alkaloids","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"National Research Council Canada; Government of Canada","funders":"","keywords":"Rudder; Set (abstract data type); Polynomial; Variable (mathematics); Regression analysis; Data set; Harmonic","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"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":[0.005487042,0.001987764,0.001188882,0.002195005,0.001030988,0.001164666,0.00171961,0.0008464701,0.04060261],"category_scores_gemma":[0.02374423,0.001145868,0.001620314,0.002975059,0.0006199134,0.00126102,0.00197627,0.002884949,0.01998871],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006673832,"about_ca_system_score_gemma":0.002745899,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003979716,"about_ca_topic_score_gemma":0.005466031,"domain_scores_codex":[0.9951892,0.001733907,0.0003590375,0.0009936503,0.001536249,0.0001879983],"domain_scores_gemma":[0.9937527,0.003148037,0.0004146197,0.0009863186,0.001599843,0.00009850322],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000434443,0.0003444043,0.004631213,0.001054977,0.0002685423,0.0003345211,0.0004212344,0.03204891,0.02512365,0.02505471,0.04920739,0.8610759],"study_design_scores_gemma":[0.0001657208,0.0008214018,0.0128919,0.0002396841,0.0001667091,0.0008842646,0.0002897978,0.6453499,0.04112259,0.01690829,0.2809003,0.0002594288],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002013583,0.00006350217,0.9858258,0.0001063904,0.00007479631,0.0003761079,0.001216782,0.008368138,0.001954863],"genre_scores_gemma":[0.02342301,0.0001555877,0.9653128,0.00006997868,0.00004524329,0.001868089,0.001507443,0.001949034,0.005668777],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.04060261,"threshold_uncertainty_score":0.1358292,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05391075135180556,"score_gpt":0.3188447637238954,"score_spread":0.2649340123720899,"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."}}