{"id":"W4292801095","doi":"10.21203/rs.3.rs-1976226/v1","title":"A Comprehensive Data Fusion to Evaluate the Impacts of COVID-19 on Passenger Travel Demands: Application of a Core-Satellite Data Collection Paradigm","year":2022,"lang":"en","type":"preprint","venue":"Research Square","topic":"Urban Transport and Accessibility","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Data collection; Travel behavior; Survey data collection; Estimation; Computer science; Travel survey; Transport engineering; Econometrics; Operations research; Statistics; Economics; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00543225,0.001303286,0.001222997,0.001642519,0.0009474444,0.002133274,0.0009494614,0.001196364,0.001607439],"category_scores_gemma":[0.007111194,0.00043705,0.001264942,0.003135402,0.000701873,0.00266414,0.001918513,0.0009978703,0.0002682439],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001289488,"about_ca_system_score_gemma":0.003196703,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02541604,"about_ca_topic_score_gemma":0.01551487,"domain_scores_codex":[0.9980064,0.0007122322,0.0001106715,0.0003806947,0.0005555594,0.0002344862],"domain_scores_gemma":[0.9969421,0.0009443399,0.0001993723,0.0005789698,0.001139678,0.0001954665],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.002179768,0.001174836,0.05831173,0.000405229,0.000863739,0.0001978628,0.0003523888,0.7248343,0.01960138,0.01535383,0.004280104,0.1724448],"study_design_scores_gemma":[0.00005011611,0.0005457117,0.01926751,0.00002762567,0.0001404859,0.0000481779,0.0002161635,0.9676935,0.006591799,0.004072125,0.001304227,0.00004250007],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7256203,0.0005479502,0.258671,0.0007707317,0.0001841789,0.0005291416,0.004422719,0.0007799717,0.008473984],"genre_scores_gemma":[0.9336174,0.0001299167,0.06283913,0.00007880483,0.00003500724,0.0001298107,0.002534332,0.00004773045,0.0005879118],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02541604,"threshold_uncertainty_score":0.05053622,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3321380543161975,"score_gpt":0.5149286465331273,"score_spread":0.1827905922169298,"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."}}