{"id":"W4394418255","doi":"10.6084/m9.figshare.19329069","title":"Interactive visual analytics of moving passenger flocks using massive smart card data","year":2022,"lang":"en","type":"dataset","venue":"Figshare","topic":"Human Mobility and Location-Based Analysis","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University Health Network","funders":"","keywords":"Flock; Analytics; Visual analytics; Computer science; Data analysis; Data science; World Wide Web; Visualization; Data mining; Biology","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":[0.0003520957,0.0008136553,0.0003740776,0.002963219,0.0003034437,0.000834087,0.0007192954,0.0004108452,0.00233517],"category_scores_gemma":[0.001439087,0.000146278,0.000436547,0.002959473,0.0002044326,0.000926986,0.001166937,0.000477579,0.0008577862],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004335654,"about_ca_system_score_gemma":0.0003691199,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01409863,"about_ca_topic_score_gemma":0.03123872,"domain_scores_codex":[0.9998044,0.0000419821,0.00002212259,0.00005243826,0.00004986311,0.0000291977],"domain_scores_gemma":[0.9995048,0.0001471949,0.00005405433,0.0001384634,0.000100128,0.00005533988],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001266057,0.0007513845,0.1846585,0.002243671,0.0004630161,0.00189343,0.00261285,0.07370206,0.01571821,0.01542456,0.4054466,0.2958198],"study_design_scores_gemma":[0.0002410658,0.0002022268,0.2199274,0.0003093785,0.0001294748,0.0008507756,0.004954541,0.4692161,0.01407316,0.01699951,0.272929,0.0001674659],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.374019,0.00103727,0.04303504,0.001602345,0.0002964208,0.0004723596,0.5524548,0.0151917,0.01189111],"genre_scores_gemma":[0.4916306,0.0006803317,0.07038725,0.0001559909,0.00007006276,0.0003629683,0.4333535,0.0001817472,0.003177507],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.01409863,"threshold_uncertainty_score":0.02803314,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1173422039611177,"score_gpt":0.3964950763813072,"score_spread":0.2791528724201895,"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."}}