{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0004100237,0.000218899,0.0004664962,0.0002786977,0.0006166151,0.0001276849,0.001387233,0.000237244,0.7279298],"category_scores_gemma":[0.004896492,0.0002468747,0.0001895613,0.000654114,0.00005442141,0.0002819158,0.0009266002,0.0006254601,0.0001913504],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004507372,"about_ca_system_score_gemma":0.001365349,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.01732351,"about_ca_topic_score_gemma":0.02715744,"domain_scores_codex":[0.9973575,0.0005466252,0.0004208344,0.0005823813,0.0007985468,0.0002940574],"domain_scores_gemma":[0.9971201,0.0008098708,0.0005907923,0.001012231,0.0003512886,0.0001157363],"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":[0.000006039384,0.00007082103,0.00001012923,0.0001690802,0.0002106176,0.00001641643,0.0005945212,0.000550569,9.119294e-7,6.386592e-7,0.9980165,0.0003537049],"study_design_scores_gemma":[0.00005871794,0.00001939453,0.00002589931,0.0004756648,0.0002832345,2.378561e-7,0.004242404,0.004316311,0.000004270605,0.000005760304,0.9903148,0.0002532857],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00002433577,0.0000956756,0.000003315815,0.00004931195,0.0001292876,0.0002324689,0.9992207,0.00001959016,0.0002252908],"genre_scores_gemma":[0.002712233,0.00001446847,0.00001236474,0.00008178387,0.0003617376,0.00004043843,0.9966453,0.00001510488,0.0001165549],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.7277384,"threshold_uncertainty_score":0.9999983,"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."}}