{"id":"W4390100407","doi":"10.1145/3589132.3625619","title":"Point2Hex: Higher-order Mobility Flow Data and Resources","year":2023,"lang":"en","type":"article","venue":"","topic":"Human Mobility and Location-Based Analysis","field":"Social Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"","keywords":"Computer science; Data mining; Documentation; Cluster analysis; Granularity; Anomaly detection; Overfitting; Machine learning; Information retrieval; Programming language","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":[0.001672819,0.001555572,0.0007906009,0.003424201,0.0008138403,0.001831082,0.002667968,0.001012258,0.0130685],"category_scores_gemma":[0.0106526,0.000996989,0.002131392,0.00527922,0.0007088515,0.002504152,0.003102548,0.002071305,0.00814036],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007974982,"about_ca_system_score_gemma":0.001805314,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009960823,"about_ca_topic_score_gemma":0.01494634,"domain_scores_codex":[0.9989336,0.0001596941,0.0001204798,0.0003271147,0.0003514164,0.0001076093],"domain_scores_gemma":[0.9970121,0.0009333831,0.0001797727,0.001113237,0.0005823647,0.0001789803],"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":[0.001242336,0.0006307592,0.03441503,0.002812728,0.0006046499,0.001206889,0.002116341,0.1271365,0.009115822,0.04239445,0.596787,0.1815376],"study_design_scores_gemma":[0.000648304,0.0003586137,0.02677641,0.0004152556,0.0001536589,0.001020787,0.001113759,0.403074,0.03171068,0.07451793,0.4598454,0.0003652901],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.03649466,0.0003225805,0.2968016,0.000761896,0.0005492676,0.0008808585,0.4962029,0.1597235,0.008262802],"genre_scores_gemma":[0.07828123,0.0003971072,0.2806492,0.0001785311,0.00006787622,0.001668093,0.6270011,0.008646369,0.003110424],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.0130685,"threshold_uncertainty_score":0.04371846,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05839212160918147,"score_gpt":0.3439364566921072,"score_spread":0.2855443350829256,"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."}}