{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","sts","scholarly_communication"],"consensus_categories":["metaresearch","sts"],"category_scores_codex":[0.03898614,0.00012439,0.0002246515,0.0008396496,0.01978708,0.002317003,0.003305202,0.00006702516,0.00006033645],"category_scores_gemma":[0.0241206,0.0001254995,0.0000841124,0.002141053,0.01335541,0.001859875,0.0003594492,0.0002773463,0.00001580143],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008038576,"about_ca_system_score_gemma":0.002901226,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006892683,"about_ca_topic_score_gemma":0.01053466,"domain_scores_codex":[0.9952363,0.0002206734,0.0003989246,0.0008692588,0.001957705,0.001317082],"domain_scores_gemma":[0.9967272,0.0008424116,0.000223924,0.0008727622,0.001013311,0.0003203677],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00005009141,0.0001483204,0.533597,0.0001076212,0.000009482054,0.00001203131,0.3143012,0.0002734314,0.01722095,0.09163412,0.0006670551,0.04197875],"study_design_scores_gemma":[0.002480484,0.0004111145,0.5701697,0.001627305,0.00006086431,0.000003420361,0.1271354,0.1667426,0.02088096,0.06852396,0.03968574,0.00227849],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.959755,0.00004943411,0.00132548,0.003298159,0.0003185494,0.0007008297,0.000006524258,0.00006827895,0.03447775],"genre_scores_gemma":[0.9965678,0.000009328513,0.0006898962,0.000067826,0.0003796427,0.00005176858,6.525405e-7,0.000008734964,0.002224375],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1871658,"threshold_uncertainty_score":0.9997205,"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."}}