{"id":"W3199011366","doi":"10.1109/icisce50968.2020.00049","title":"Air Temperature Forecasting using Traditional and Deep Learning Algorithms","year":2020,"lang":"en","type":"article","venue":"","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba; University of Toronto","funders":"","keywords":"Mean squared error; Machine learning; Artificial intelligence; Computer science; Air temperature; Deep learning; Algorithm; Atmospheric model; Meteorology; Mathematics; Statistics","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":[0.0005344083,0.0007043653,0.0004987338,0.0006424757,0.000254028,0.0006527353,0.0007663431,0.000583296,0.0009504786],"category_scores_gemma":[0.001296411,0.0002444397,0.0004442239,0.00076816,0.000268607,0.001321037,0.0005529844,0.0007650178,0.0003097709],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000768784,"about_ca_system_score_gemma":0.0005197565,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008698921,"about_ca_topic_score_gemma":0.008246274,"domain_scores_codex":[0.9997134,0.00004414365,0.00002109102,0.00009645059,0.00008348488,0.00004142793],"domain_scores_gemma":[0.9996487,0.0001443812,0.00004128843,0.00002998013,0.0001189452,0.00001684222],"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":[0.0001206585,0.00007914651,0.003306329,0.00009382027,0.00007234239,0.00005374559,0.00002990879,0.7708605,0.003833495,0.002351156,0.001333625,0.2178653],"study_design_scores_gemma":[0.000002275724,0.000009121703,0.0002333285,0.000002492273,0.000003381067,0.000003520599,0.000002476521,0.9984502,0.0005556943,0.0005602239,0.0001753961,0.000001897819],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1670282,0.003011492,0.8215676,0.0005546469,0.0002876445,0.00005120212,0.000349953,0.001790019,0.005359275],"genre_scores_gemma":[0.881191,0.001067232,0.1132888,0.0001495007,0.0001442114,0.00005229304,0.0004309727,0.000058341,0.003617714],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008698921,"threshold_uncertainty_score":0.01729655,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03766111911098595,"score_gpt":0.2017424672401741,"score_spread":0.1640813481291881,"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."}}