{"id":"W6980942122","doi":"","title":"Deep Decarbonization in Cities: Greenhouse Gas Emissions Measurement, Monitoring, and Reporting","year":2023,"lang":"en","type":"dissertation","venue":"UWSpace (University of Waterloo)","topic":"Policy Transfer and Learning","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Mitacs","keywords":"Documentation; Milestone; Greenhouse gas; Scope (computer science); Best practice; Inclusion (mineral); Climate change; Action (physics); Global warming","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":[0.02050159,0.0003623561,0.000367721,0.003633393,0.009667148,0.007439723,0.002319828,0.0008196829,0.001946601],"category_scores_gemma":[0.04349137,0.000412902,0.0003596701,0.01231488,0.006082089,0.003743032,0.003672277,0.001912348,0.0002167878],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.08480873,"about_ca_system_score_gemma":0.1413504,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.9403906,"about_ca_topic_score_gemma":0.9557189,"domain_scores_codex":[0.9710115,0.01033612,0.001481606,0.001530911,0.01289749,0.002742375],"domain_scores_gemma":[0.9539084,0.01373876,0.007075377,0.002736443,0.0196663,0.002874681],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"observational","study_design_scores_codex":[0.0001594646,0.0002112889,0.240189,0.002716085,0.0001009472,0.001190278,0.3706043,0.004810615,0.004590847,0.05694399,0.03692709,0.2815561],"study_design_scores_gemma":[0.00001906806,0.0001020715,0.3190531,0.002097041,0.00009575884,0.0001770935,0.3886626,0.003206972,0.003358161,0.007139681,0.2758454,0.0002431449],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7662106,0.006530122,0.01838768,0.05653919,0.0003384902,0.001784418,0.004399943,0.0003807733,0.1454288],"genre_scores_gemma":[0.9823156,0.002760841,0.00917711,0.001188175,0.00003481772,0.0002250396,0.0005822588,0.00003641378,0.00367973],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9403906,"threshold_uncertainty_score":0.6153333,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05144882763958328,"score_gpt":0.2899561290811422,"score_spread":0.2385073014415589,"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."}}