{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004670496,0.00008632639,0.00009006124,0.00007659405,0.0007739825,0.00006123803,0.000115756,0.000083523,0.0001161741],"category_scores_gemma":[0.00006766737,0.00005796757,0.00006772781,0.0004347392,0.000148047,0.00007306889,0.000005453684,0.00007195529,0.00003826954],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006332445,"about_ca_system_score_gemma":0.00007585796,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001013051,"about_ca_topic_score_gemma":0.008654606,"domain_scores_codex":[0.9990052,0.000216384,0.00009171722,0.0002075363,0.0002848934,0.0001942654],"domain_scores_gemma":[0.9992421,0.0003800447,0.00004736707,0.0001701862,0.0001096738,0.00005062409],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004789712,0.0003328123,0.005777823,0.0001472221,0.00038848,0.000008453384,0.1175007,0.1370766,0.001794844,0.6317076,0.05716313,0.0476233],"study_design_scores_gemma":[0.004076377,0.0008896271,0.01464533,0.0003770115,0.0008499054,0.000001857431,0.1534195,0.5664563,0.008794889,0.1068631,0.1415149,0.002111245],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9733725,0.00003891347,0.003973846,0.01724791,0.0001374868,0.000580348,0.00004482602,0.0002146978,0.004389495],"genre_scores_gemma":[0.993468,0.00007265861,0.00007693377,0.0005882697,0.000128762,0.00008689688,0.0001264364,0.000009985143,0.005442077],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5248445,"threshold_uncertainty_score":0.5952926,"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."}}