{"id":"W3087514398","doi":"10.3390/su12187769","title":"Seasonality Effect Analysis and Recognition of Charging Behaviors of Electric Vehicles: A Data Science Approach","year":2020,"lang":"en","type":"article","venue":"Sustainability","topic":"Electric Vehicles and Infrastructure","field":"Engineering","cited_by":26,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Trois-Rivières","funders":"Departamento Administrativo de Ciencia, Tecnología e Innovación (COLCIENCIAS)","keywords":"Random forest; Mean absolute percentage error; Mean squared error; Poisson regression; Statistics; Benchmark (surveying); Electrical load; Principal component analysis; Linear regression; Lasso (programming language); Computer science; Econometrics; Mathematics; Machine learning; Engineering; Geography","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":[0.001167224,0.0005367433,0.0003915172,0.002291221,0.000309458,0.000887435,0.0005825778,0.0007882445,0.0006820747],"category_scores_gemma":[0.003166825,0.0001793849,0.0008270339,0.001978246,0.0003411479,0.0008863794,0.0004774484,0.0007552062,0.0003430625],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006592573,"about_ca_system_score_gemma":0.0005071079,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009918234,"about_ca_topic_score_gemma":0.0128539,"domain_scores_codex":[0.9993519,0.0001831234,0.00007579847,0.0002088031,0.0001216926,0.00005872628],"domain_scores_gemma":[0.9980718,0.0009316821,0.0002870985,0.0003281185,0.0003323401,0.00004899166],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0004479051,0.001346519,0.4644525,0.0005179933,0.0005201052,0.0005686824,0.0005395444,0.2140576,0.009512243,0.005506277,0.01284095,0.2896896],"study_design_scores_gemma":[0.00001574237,0.0002053321,0.1966788,0.00006725766,0.00008194571,0.0002513325,0.001000147,0.7820413,0.005654311,0.005392835,0.008560503,0.00005052006],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.832397,0.001089371,0.1405692,0.00176489,0.0002229055,0.0002293649,0.01723707,0.0009093455,0.005580715],"genre_scores_gemma":[0.9455394,0.00039669,0.0386415,0.0001056168,0.00009029025,0.0001342028,0.0139788,0.00002979005,0.001083823],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.009918234,"threshold_uncertainty_score":0.01972103,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01156491746724451,"score_gpt":0.2459230861530017,"score_spread":0.2343581686857572,"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."}}