{"id":"W4205588374","doi":"10.36227/techrxiv.14398304","title":"Predicting Rainfall using Machine Learning Techniques","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science; Reliability (semiconductor); Machine learning; Set (abstract data type); Artificial intelligence","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":[0.0008266833,0.0007292145,0.0005572203,0.001672682,0.00028758,0.001135693,0.0005980905,0.0007420481,0.001236478],"category_scores_gemma":[0.003037584,0.00024866,0.0005093628,0.001364908,0.000188434,0.00104427,0.0003492526,0.0008254786,0.0006490088],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004961435,"about_ca_system_score_gemma":0.0004562391,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00864647,"about_ca_topic_score_gemma":0.005787949,"domain_scores_codex":[0.9995176,0.0001463114,0.00004155759,0.0001008229,0.0001388097,0.00005496929],"domain_scores_gemma":[0.998125,0.001304712,0.0001506587,0.0001252033,0.0002631168,0.00003131706],"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":[0.0001296402,0.0002173915,0.01430899,0.00007431585,0.00007471217,0.0001062168,0.0000604349,0.7706859,0.005313575,0.0008487434,0.001917323,0.2062627],"study_design_scores_gemma":[0.000003393016,0.00001978969,0.001293309,0.000004081781,0.000005063525,0.000008854016,0.00001452335,0.9964002,0.001450673,0.000506059,0.0002884659,0.000005564022],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.455368,0.001373096,0.525302,0.001198473,0.0002294575,0.0002260924,0.001728267,0.005038965,0.009535685],"genre_scores_gemma":[0.892765,0.0005136527,0.1033457,0.0000743782,0.00009134821,0.00006178283,0.001097369,0.00004695428,0.002003897],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00864647,"threshold_uncertainty_score":0.01719224,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0185284624655089,"score_gpt":0.2325997206975775,"score_spread":0.2140712582320685,"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."}}