{"id":"W2164203674","doi":"10.1175/2010mwr3130.1","title":"Evolutionary Optimization of an Ice Accretion Forecasting System","year":2010,"lang":"en","type":"article","venue":"Monthly Weather Review","topic":"Icing and De-icing Technologies","field":"Engineering","cited_by":23,"is_retracted":false,"has_abstract":true,"ca_institutions":"Alberta Environment and Protected Areas; University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Icing; Meteorology; Freezing rain; Numerical weather prediction; Accretion (finance); Environmental science; Storm; Precipitation; Computer science; Consistency (knowledge bases); Climatology; Geology; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0008963964,0.0005798056,0.0006458542,0.0005166752,0.0005061501,0.0007520008,0.0007229439,0.0009058935,0.001731732],"category_scores_gemma":[0.002305364,0.0003150819,0.0003337586,0.000368428,0.000442053,0.0003635256,0.0006608051,0.0004710345,0.0001924934],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009783575,"about_ca_system_score_gemma":0.0009773071,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01517849,"about_ca_topic_score_gemma":0.005496719,"domain_scores_codex":[0.9996848,0.000105845,0.00001583778,0.00007076046,0.00005887085,0.00006388165],"domain_scores_gemma":[0.9993159,0.0003152511,0.00007532971,0.00004434309,0.0002083092,0.00004096305],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0000215423,0.0000170308,0.0003964152,0.000006984063,0.000008870511,0.00002894759,0.00001547613,0.9891889,0.0005576264,0.0007791453,0.0001291386,0.008849965],"study_design_scores_gemma":[0.000004312383,0.000009559541,0.00005976104,9.248038e-7,0.000002187841,0.00000210675,0.000002332156,0.9995978,0.0001289527,0.0001215966,0.000069629,9.117746e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3610925,0.000117331,0.6237955,0.0004196246,0.00005753818,0.000192098,0.0001414237,0.0006625836,0.01352135],"genre_scores_gemma":[0.9265807,0.00003712082,0.06996237,0.00006176143,0.00001603956,0.0001628218,0.00009838539,0.00002315387,0.00305772],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01517849,"threshold_uncertainty_score":0.03018034,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01565107209653203,"score_gpt":0.2200059409815732,"score_spread":0.2043548688850412,"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."}}