{"id":"W3200873835","doi":"10.1016/j.trip.2021.100465","title":"Canadian transit agencies response to COVID-19: Understanding strategies, information accessibility and the use of social media","year":2021,"lang":"en","type":"article","venue":"Transportation Research Interdisciplinary Perspectives","topic":"COVID-19 epidemiological studies","field":"Mathematics","cited_by":25,"is_retracted":false,"has_abstract":true,"ca_institutions":"Memorial University of Newfoundland; University of Saskatchewan","funders":"Canadian Institutes of Health Research; Natural Sciences and Engineering Research Council of Canada; Mitacs","keywords":"Transit (satellite); Social media; USable; Public transport; Business; Public relations; Workforce; Political science; Transport engineering; Computer science; Engineering; World Wide Web","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":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.004096266,0.0001639527,0.0003938781,0.0003387646,0.001079316,0.0001654586,0.000228307,0.0001062258,0.0002396575],"category_scores_gemma":[0.01174114,0.0001178616,0.0001284656,0.0008227249,0.001114957,0.0007885027,0.00008426901,0.0003784827,0.000002779296],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001759969,"about_ca_system_score_gemma":0.001589748,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.007770649,"about_ca_topic_score_gemma":0.3618009,"domain_scores_codex":[0.9966564,0.001445438,0.0005826456,0.00034537,0.0005692869,0.0004008413],"domain_scores_gemma":[0.9855016,0.01321642,0.0001092488,0.0002479255,0.0005877303,0.0003370481],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"qualitative","study_design_scores_codex":[0.003974979,0.00005210958,0.0009363278,0.0002632707,0.0001098753,0.00003252691,0.6392562,0.0002732118,0.0001785159,0.3516421,0.00316086,0.0001200036],"study_design_scores_gemma":[0.0008013824,0.00007978162,0.1358573,0.00003623251,0.00003016233,0.000001284112,0.6199529,0.0002201162,0.00002995009,0.2421517,0.0007053474,0.0001338656],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8650587,0.0001209259,0.04079927,0.09215868,0.00004977348,0.000854944,0.0004427887,0.00007373033,0.0004411362],"genre_scores_gemma":[0.9985296,0.00005064902,0.0009959341,0.0002322724,0.00002604055,0.00008054455,0.00002943179,0.000009349489,0.0000461872],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3540303,"threshold_uncertainty_score":0.9988367,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.5342200579661311,"score_gpt":0.5223233954564706,"score_spread":0.01189666250966059,"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."}}