{"id":"W3098478222","doi":"10.1002/wcc.688","title":"Using big data analytics to synthesize research domains and identify emerging fields in urban climatology","year":2020,"lang":"en","type":"article","venue":"Wiley Interdisciplinary Reviews Climate Change","topic":"Urban Heat Island Mitigation","field":"Environmental Science","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"Global Institute for Water Security; University of Saskatchewan","funders":"Global Water Futures; Natural Sciences and Engineering Research Council of Canada","keywords":"Urbanization; Climate change; Flooding (psychology); Flood myth; Downscaling; Climate model; Urban planning; Urban studies; Environmental science; Social network analysis; Analytics; Data science; Geography; Environmental resource management; Climatology; Environmental planning; Precipitation; Meteorology; Computer science; Social media; Ecology","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":["bibliometrics"],"consensus_categories":[],"category_scores_codex":[0.01195377,0.001023734,0.001310823,0.03975883,0.0009747103,0.007274938,0.001064711,0.0007909506,0.002115476],"category_scores_gemma":[0.03300201,0.0003810675,0.001963787,0.03783104,0.0009144399,0.005524849,0.00361769,0.00126228,0.0004301311],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0018668,"about_ca_system_score_gemma":0.00313685,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003338347,"about_ca_topic_score_gemma":0.006387207,"domain_scores_codex":[0.9927682,0.003465707,0.001369204,0.001031196,0.00120083,0.0001649631],"domain_scores_gemma":[0.9492427,0.03733132,0.004715533,0.002780691,0.005240945,0.0006889096],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0002551529,0.0001892044,0.1051157,0.0548565,0.00383908,0.00115083,0.01139641,0.01511936,0.004470221,0.09752952,0.03594564,0.6701323],"study_design_scores_gemma":[0.0001276054,0.0002067577,0.1190358,0.02535919,0.002891182,0.0006287927,0.0272453,0.06324184,0.005290268,0.4086982,0.3469531,0.0003219876],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1946132,0.2492617,0.3791187,0.03081607,0.002703511,0.002533618,0.09875307,0.002873954,0.03932615],"genre_scores_gemma":[0.5086255,0.07823905,0.3607855,0.002987843,0.001755152,0.0035001,0.04194279,0.0003083821,0.001855634],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9602412,"threshold_uncertainty_score":0.06321836,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.4561668410288036,"score_gpt":0.4443481280962206,"score_spread":0.01181871293258302,"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."}}