{"id":"W3111885837","doi":"10.32866/001c.18066","title":"Travel Survey Recruitment Through Facebook and Transit app: Lessons from COVID-19","year":2020,"lang":"en","type":"article","venue":"Findings","topic":"Privacy, Security, and Data Protection","field":"Social Sciences","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"The Scarborough Hospital; University of Toronto","funders":"","keywords":"Demographics; Sample (material); Coronavirus disease 2019 (COVID-19); Smartphone app; Survey data collection; Business; Transit (satellite); Survey sampling; Advertising; Public transport; Geography; Transport engineering; Internet privacy; Computer science; Engineering; Medicine; Demography; Statistics; Environmental health; Sociology; Mathematics","routes":{"ca_aff":true,"ca_fund":false,"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":[],"consensus_categories":[],"category_scores_codex":[0.1273582,0.0007509224,0.0009212022,0.001815557,0.0042715,0.006192576,0.005290711,0.003378569,0.009037498],"category_scores_gemma":[0.2538296,0.001180249,0.0008748898,0.002350857,0.002673394,0.008107757,0.007224455,0.004399996,0.004596933],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004249366,"about_ca_system_score_gemma":0.008973344,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04026587,"about_ca_topic_score_gemma":0.09176125,"domain_scores_codex":[0.8912984,0.09139608,0.003201755,0.003364756,0.007498268,0.003240797],"domain_scores_gemma":[0.6934983,0.2347897,0.006955425,0.0308573,0.02372374,0.01017559],"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":[0.0008869673,0.002177494,0.2081619,0.001834673,0.00018904,0.00102782,0.1464432,0.001469589,0.00116398,0.01206711,0.2112458,0.4133325],"study_design_scores_gemma":[0.000639843,0.002981766,0.2999029,0.007835637,0.0003174475,0.001698478,0.2190542,0.0116183,0.005519814,0.03745652,0.4124003,0.0005749135],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5342256,0.002576201,0.06887781,0.2655663,0.003625985,0.01486199,0.01316147,0.002480362,0.09462418],"genre_scores_gemma":[0.7535411,0.002018944,0.1449642,0.0534665,0.001414516,0.01927497,0.006189599,0.001748291,0.01738187],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1273582,"threshold_uncertainty_score":0.6735427,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2822915832814001,"score_gpt":0.3949947310162585,"score_spread":0.1127031477348584,"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."}}