{"id":"W4399259911","doi":"10.1007/s12652-024-04814-x","title":"Non intrusive load monitoring using additive time series modeling via finite mixture models aggregation","year":2024,"lang":"en","type":"article","venue":"Journal of Ambient Intelligence and Humanized Computing","topic":"Smart Grid Energy Management","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Series (stratigraphy); Computer science; Time series; Biological system; Machine learning; Geology","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0003858165,0.0002290742,0.0002929858,0.0003020236,0.0001652263,0.0002717962,0.0001659509,0.00007377812,0.00001875133],"category_scores_gemma":[0.00002142593,0.0002223942,0.0001065618,0.0002434863,0.00003044188,0.0008016204,0.0001010309,0.0003931778,0.000009672944],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002994455,"about_ca_system_score_gemma":0.00003596518,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001464457,"about_ca_topic_score_gemma":0.000001392785,"domain_scores_codex":[0.9985667,0.00002199772,0.0006042618,0.0001904902,0.000351826,0.0002647439],"domain_scores_gemma":[0.9993365,0.00008929999,0.0001247727,0.0001053018,0.000264944,0.00007912361],"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":[0.00001829533,0.000008645823,0.000007418732,0.00009257415,0.0001503694,0.00006415312,0.003407537,0.9711038,0.002511165,0.000255901,0.00003172745,0.02234841],"study_design_scores_gemma":[0.00007935263,0.00007656428,0.000004696634,0.001417164,0.00007665158,0.00008383517,0.0005992843,0.9819378,0.01273472,0.002662883,0.0001148822,0.0002121469],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3242956,0.001688817,0.6724529,0.00001301858,0.001213908,0.00007222857,0.000001231409,0.00008105492,0.0001812928],"genre_scores_gemma":[0.9869447,0.0006193838,0.0112567,0.00001332476,0.001088309,0.000001053974,0.000002094439,0.00004259958,0.00003183193],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6626492,"threshold_uncertainty_score":0.9068974,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02276864922019724,"score_gpt":0.2426570906897408,"score_spread":0.2198884414695435,"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."}}