{"id":"W2508090158","doi":"10.1145/2934328.2934333","title":"Understanding solar PV and battery adoption in Ontario","year":2016,"lang":"en","type":"article","venue":"","topic":"Innovation Diffusion and Forecasting","field":"Decision Sciences","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Photovoltaic system; Battery (electricity); Carbon footprint; Electricity; Environmental economics; Grid; Dependency (UML); Computer science; Business; Automotive engineering; Greenhouse gas; Engineering; Power (physics); Electrical engineering; Economics","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"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":[0.0005487708,0.00016172,0.0002092,0.0004257281,0.0008506727,0.001166563,0.0005636874,0.0004288136,0.002640952],"category_scores_gemma":[0.002918232,0.0001943095,0.0003267126,0.001608339,0.0006245744,0.0007738792,0.0005402337,0.0003355527,0.0001519258],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.02791854,"about_ca_system_score_gemma":0.01334773,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.982708,"about_ca_topic_score_gemma":0.9874154,"domain_scores_codex":[0.9996493,0.00006120298,0.00001330852,0.0000425238,0.0000921672,0.0001415104],"domain_scores_gemma":[0.9984697,0.0006159658,0.0003579065,0.00005976183,0.0003404472,0.0001562692],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002092858,0.0002170809,0.8623068,0.0001476416,0.00009767785,0.0005566172,0.009255815,0.07899965,0.002219358,0.01323753,0.003037875,0.02971472],"study_design_scores_gemma":[0.00004394651,0.0001048823,0.8647201,0.00006211462,0.00007654655,0.00006579133,0.01095147,0.1046401,0.0006248295,0.002559054,0.01609661,0.00005471484],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9914848,0.0001536289,0.0005775678,0.0006408166,0.000001985742,0.00002258005,0.0005867104,0.000008207707,0.006523648],"genre_scores_gemma":[0.9979882,0.0002317304,0.0002521068,0.00002475396,0.000001542694,0.000008496524,0.0001813773,0.000002027763,0.00130979],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02791854,"threshold_uncertainty_score":0.2025641,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.4011360591557662,"score_gpt":0.3460615058287001,"score_spread":0.05507455332706612,"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."}}