{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008591562,0.0001286903,0.0002727835,0.0003463924,0.0004971955,0.00003027732,0.0001682221,0.0002387749,0.00003133887],"category_scores_gemma":[0.000394739,0.0001661373,0.00007068453,0.0003832516,0.00008583053,0.000176544,0.00002046136,0.0002454584,0.000004893057],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001683365,"about_ca_system_score_gemma":0.0002049493,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.3845454,"about_ca_topic_score_gemma":0.579442,"domain_scores_codex":[0.9986229,0.0001388286,0.0002290201,0.0002564433,0.0004690058,0.0002838092],"domain_scores_gemma":[0.9992103,0.00004245516,0.0003712193,0.0001136431,0.0001500674,0.0001122917],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"qualitative","study_design_scores_codex":[0.00002997136,0.00002056553,0.05964326,0.0001606203,0.00003575394,0.00004123437,0.9339742,0.0000649217,0.0008183071,0.0001201241,0.00006444663,0.005026574],"study_design_scores_gemma":[0.0003147993,0.00003024309,0.09016696,0.0006768007,0.0000753061,3.793056e-7,0.9075366,0.00009555103,0.0001887304,0.0002460198,0.0004323737,0.0002362079],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.996848,0.0001194302,0.00001294351,0.0002937098,0.000255776,0.0001621334,0.000001765528,0.0001045533,0.002201621],"genre_scores_gemma":[0.8706746,0.00110998,0.0001655311,0.000002145161,0.00009305289,6.585427e-7,0.00003717475,0.00002747193,0.1278895],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1948966,"threshold_uncertainty_score":0.6774883,"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."}}