{"id":"W4280497094","doi":"10.1155/2022/4217431","title":"Cluster Analysis of Daily Cycling Flow Profiles during COVID-19 Lockdown in the UK","year":2022,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Urban Transport and Accessibility","field":"Social Sciences","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Engineering and Physical Sciences Research Council","keywords":"Cycling; Coronavirus disease 2019 (COVID-19); Cluster (spacecraft); Pandemic; Flow (mathematics); Material flow analysis; Demography; Geography; Environmental science; Medicine; Mathematics; Computer science; Biology; Ecology; Internal medicine; Sociology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007332527,0.0002878403,0.0004283078,0.002505561,0.0007028359,0.001015134,0.0003898833,0.0002999101,0.001996614],"category_scores_gemma":[0.003207413,0.0001371428,0.0004073236,0.003009307,0.0003815293,0.0003236543,0.0008473441,0.0002550346,0.0005038655],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001414242,"about_ca_system_score_gemma":0.0009926325,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1410006,"about_ca_topic_score_gemma":0.1467575,"domain_scores_codex":[0.9991471,0.0001652097,0.00007222051,0.0002155975,0.0002162937,0.0001835861],"domain_scores_gemma":[0.9982805,0.0003010923,0.0002559908,0.0001322012,0.0008436746,0.0001865773],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001466965,0.0001221923,0.8935377,0.0002397395,0.0001570668,0.0005782605,0.01082423,0.004869478,0.004797982,0.0007812646,0.008496729,0.07412841],"study_design_scores_gemma":[0.0000060677,0.00009149559,0.989897,0.00002621892,0.00001135807,0.00006759928,0.004167721,0.003391668,0.0002919761,0.00009621668,0.001924884,0.00002781915],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9917518,0.00009814939,0.001715128,0.00008605861,0.0000229669,0.0001430747,0.004367054,0.00006990319,0.001745849],"genre_scores_gemma":[0.9915906,0.00007991014,0.001504799,0.00001472988,0.000009173927,0.0001208167,0.005069755,0.00001956671,0.001590539],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1410006,"threshold_uncertainty_score":0.2803598,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0173534698764806,"score_gpt":0.3158816661214012,"score_spread":0.2985281962449206,"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."}}