{"id":"W4387458188","doi":"10.3390/s23198296","title":"Unsupervised Mixture Models on the Edge for Smart Energy Consumption Segmentation with Feature Saliency","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Human Mobility and Location-Based Analysis","field":"Social Sciences","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Cluster analysis; Computer science; Smart meter; Mixture model; Data mining; Feature (linguistics); Energy consumption; Feature selection; Granularity; Robustness (evolution); Cloud computing; Metering mode; Artificial intelligence; Smart grid; Engineering","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":[0.00107114,0.001046731,0.001402134,0.001190175,0.0005904853,0.001231957,0.002003432,0.001203399,0.001849446],"category_scores_gemma":[0.003472997,0.0006691061,0.001511394,0.001308498,0.0007225629,0.001793203,0.001596598,0.001592733,0.001084727],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008374394,"about_ca_system_score_gemma":0.0006992102,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008837571,"about_ca_topic_score_gemma":0.01052725,"domain_scores_codex":[0.9994869,0.0001559817,0.00002365034,0.0001569576,0.0001035561,0.00007303007],"domain_scores_gemma":[0.999141,0.00051717,0.00007877215,0.00009547253,0.0001273286,0.00004034542],"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":[0.0004632791,0.0001556127,0.003607596,0.000111943,0.0001571638,0.0001601382,0.0002618073,0.7339822,0.00642259,0.01733397,0.003947302,0.2333964],"study_design_scores_gemma":[0.000003172304,0.00000734607,0.0001777229,0.000002831663,0.000005234625,0.00001112891,0.000007385984,0.9963107,0.0004137533,0.002736715,0.0003192843,0.000004769064],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01926923,0.0003801869,0.9781677,0.0001787,0.00003192985,0.00003428612,0.00009788581,0.001051715,0.0007884128],"genre_scores_gemma":[0.6333526,0.0005174179,0.3596211,0.0003486396,0.0001257675,0.0001628949,0.001223987,0.000427366,0.004220146],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008837571,"threshold_uncertainty_score":0.01757228,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04213594891033236,"score_gpt":0.2928716167612525,"score_spread":0.2507356678509201,"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."}}