{"id":"W4402577980","doi":"10.1016/j.egycc.2024.100158","title":"Exploring attitudes and behavioral patterns in residential energy consumption: Data-driven by a machine learning approach","year":2024,"lang":"en","type":"article","venue":"Energy and Climate Change","topic":"Human Mobility and Location-Based Analysis","field":"Social Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Consumption (sociology); Energy consumption; Psychology; Energy (signal processing); Computer science; Data science; Engineering; Sociology; Social science; 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":[0.002921116,0.0005059507,0.00051962,0.0009957141,0.0002011482,0.0006342992,0.000754388,0.000692917,0.0009824716],"category_scores_gemma":[0.009390736,0.0002486242,0.0009159632,0.001366375,0.0003059072,0.0005669441,0.0003738231,0.0009603405,0.0003369199],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004535094,"about_ca_system_score_gemma":0.0005338195,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007865551,"about_ca_topic_score_gemma":0.00784859,"domain_scores_codex":[0.9988481,0.0007488695,0.00005476571,0.0001722944,0.0001253419,0.0000505761],"domain_scores_gemma":[0.9953453,0.003590631,0.000267793,0.0003936011,0.0003447733,0.00005785181],"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":[0.0006562258,0.002029383,0.3371944,0.0002648201,0.0005439043,0.0003088539,0.0007260894,0.472998,0.006086271,0.005015665,0.001479116,0.1726973],"study_design_scores_gemma":[0.000008597048,0.0001053788,0.02806362,0.00001214084,0.00002132704,0.00003887098,0.0001076957,0.9686636,0.0009926659,0.001653808,0.0003161025,0.00001623884],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8113096,0.0001293379,0.185493,0.0003141361,0.00003214401,0.0001758866,0.001029286,0.0004873045,0.001029344],"genre_scores_gemma":[0.9606799,0.00005888871,0.03766452,0.00003383236,0.00001110401,0.0001471597,0.001002712,0.00001728529,0.0003846132],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.007865551,"threshold_uncertainty_score":0.01563954,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2139477786204634,"score_gpt":0.3559625867524823,"score_spread":0.1420148081320188,"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."}}