{"id":"W3080550557","doi":"10.3390/su12176824","title":"Best–Worst Method for Modelling Mobility Choice after COVID-19: Evidence from Italy","year":2020,"lang":"en","type":"article","venue":"Sustainability","topic":"Urban Transport and Accessibility","field":"Social Sciences","cited_by":132,"is_retracted":false,"has_abstract":true,"ca_institutions":"Okanagan University College; University of British Columbia, Okanagan Campus; University of British Columbia","funders":"","keywords":"Coronavirus disease 2019 (COVID-19); Context (archaeology); Resilience (materials science); Mode (computer interface); Computer science; Mode choice; Pandemic; 2019-20 coronavirus outbreak; Operations research; Environmental economics; Business; Geography; Transport engineering; Economics; Public transport; Engineering","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":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.004765444,0.0002768062,0.0004733315,0.00003513176,0.0009024949,0.0001729281,0.0007349952,0.0002577634,0.0006835175],"category_scores_gemma":[0.02406606,0.0002725023,0.0003689155,0.0005994565,0.0006991372,0.001026468,0.0001030122,0.0003831376,0.000009123383],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001524915,"about_ca_system_score_gemma":0.002832385,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.06120106,"about_ca_topic_score_gemma":0.01061789,"domain_scores_codex":[0.9957474,0.001035424,0.0006130438,0.001299014,0.0005558905,0.0007492633],"domain_scores_gemma":[0.9927442,0.004342494,0.0001631136,0.0006861831,0.001027895,0.001036137],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.001036586,0.0003808697,0.9471393,0.001108076,0.00004133368,0.00001544917,0.03969428,0.004664754,0.00002110395,0.0009180393,0.0002623055,0.004717947],"study_design_scores_gemma":[0.002027274,0.0004409617,0.1480871,0.0001071758,0.0005881817,1.427908e-7,0.04186498,0.07563766,0.0005226684,0.6153866,0.1132267,0.002110576],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5086247,0.0006094539,0.4684859,0.01999942,0.0001152517,0.00172467,0.000121834,0.0001895591,0.0001292887],"genre_scores_gemma":[0.9838848,0.00001456954,0.01330852,0.001619304,0.0005496131,0.0003898851,0.000018914,0.00002053516,0.0001938948],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7990522,"threshold_uncertainty_score":0.9999727,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08701242483308819,"score_gpt":0.4082706804392709,"score_spread":0.3212582556061827,"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."}}