{"id":"W2800492640","doi":"10.1109/tla.2018.8358661","title":"Estimation of residential natural gas consumption in Medellín-Antioquia","year":2018,"lang":"en","type":"article","venue":"IEEE Latin America Transactions","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Autoregressive integrated moving average; Gas consumption; Quarter (Canadian coin); Support vector machine; Natural gas; Consumption (sociology); Estimation; Statistics; Econometrics; Computer science; Time series; Engineering; Geography; Mathematics; Artificial intelligence; Petroleum engineering","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00005311065,0.0001013933,0.0001426513,0.0001619467,0.00005798691,0.00001236637,0.000063263,0.0000514845,0.0002707384],"category_scores_gemma":[0.000007302503,0.0001116742,0.00005192928,0.0003220377,0.0001287829,0.0001670518,0.000001167705,0.0001531867,0.00003917452],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003223451,"about_ca_system_score_gemma":0.00001036616,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001493429,"about_ca_topic_score_gemma":0.000300925,"domain_scores_codex":[0.9993015,0.00002414773,0.0002830565,0.0001099322,0.0001172673,0.0001641028],"domain_scores_gemma":[0.9997221,0.00006239192,0.00004541257,0.0001137122,0.00002423754,0.00003209464],"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":[0.00003465554,0.00004381957,0.0006698339,0.00008537504,0.00005426434,0.000002973148,0.001625741,0.771304,0.05220937,0.00005807021,0.0002400989,0.1736718],"study_design_scores_gemma":[0.0003676612,0.00005334516,0.0053737,0.0001168171,0.00003260539,0.000008831689,0.00005415478,0.9152388,0.07811468,0.0001015171,0.0003466188,0.0001912658],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6643171,0.00006957051,0.3324957,0.00002468965,0.0009894394,0.00006806305,0.00001455764,0.000148562,0.001872321],"genre_scores_gemma":[0.9931378,0.00005922075,0.006614153,0.00001004374,0.00007305953,0.000009206316,0.00001075661,0.00001845473,0.00006726025],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3288207,"threshold_uncertainty_score":0.455394,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0132236395742077,"score_gpt":0.2453941101381197,"score_spread":0.232170470563912,"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."}}