{"id":"W2633829404","doi":"10.3390/urbansci1020021","title":"Promoting Crowdsourcing for Urban Research: Cycling Safety Citizen Science in Four Cities","year":2017,"lang":"en","type":"article","venue":"Urban Science","topic":"Human Mobility and Location-Based Analysis","field":"Social Sciences","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"Traffic Injury Research Foundation; Simon Fraser University; University of Victoria","funders":"Public Health Agency","keywords":"Promotion (chess); Outreach; Crowdsourcing; Context (archaeology); Crowds; Public relations; Citizen science; Social media; Business; Political science; World Wide Web; Computer science; Geography; Computer security","routes":{"ca_aff":true,"ca_fund":false,"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":[0.01204619,0.0007427004,0.0006777104,0.004362219,0.01066724,0.004327422,0.00220052,0.001345341,0.001965405],"category_scores_gemma":[0.01282814,0.0005360004,0.000637917,0.007435157,0.004970905,0.001479327,0.006358308,0.0009894836,0.00045674],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.02732077,"about_ca_system_score_gemma":0.03947841,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.8262783,"about_ca_topic_score_gemma":0.9151102,"domain_scores_codex":[0.9927146,0.003564707,0.0001676806,0.0007140295,0.001496421,0.00134261],"domain_scores_gemma":[0.9876593,0.003468379,0.000765127,0.001089225,0.004849622,0.002168364],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00283277,0.002950717,0.4272529,0.001266691,0.0003872403,0.001755266,0.2745795,0.01228632,0.009972278,0.006308747,0.02482654,0.2355812],"study_design_scores_gemma":[0.0004980728,0.001632892,0.5063512,0.0006669327,0.0002978821,0.0001633611,0.3822054,0.01279567,0.005908045,0.00513261,0.08389684,0.0004512309],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9824764,0.0002696367,0.002491536,0.00233012,0.00006142154,0.001302305,0.001180762,0.0001592308,0.009728722],"genre_scores_gemma":[0.9830583,0.0004034164,0.009328375,0.0004316164,0.00003431569,0.0009592266,0.001142143,0.00007441652,0.004568039],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8262783,"threshold_uncertainty_score":0.3494895,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1451079629814396,"score_gpt":0.422031018016695,"score_spread":0.2769230550352554,"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."}}