{"id":"W2346350382","doi":"10.1016/j.healthplace.2016.04.004","title":"Re-creating daily mobility histories for health research from raw GPS tracks: Validation of a kernel-based algorithm using real-life data","year":2016,"lang":"en","type":"article","venue":"Health & Place","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":18,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université de Montréal; Centre Hospitalier de l’Université de Montréal","funders":"","keywords":"Global Positioning System; Computer science; Kernel (algebra); TRIPS architecture; Raw data; Kernel density estimation; Algorithm; Mathematics; Statistics; Telecommunications","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":[0.007396181,0.000672866,0.0009524435,0.002088509,0.0004918953,0.00173807,0.001971161,0.001297564,0.0007844113],"category_scores_gemma":[0.03629036,0.0004179372,0.0009483267,0.001777585,0.0004100904,0.001855656,0.001638447,0.001067511,0.00095197],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005871475,"about_ca_system_score_gemma":0.00174778,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02025574,"about_ca_topic_score_gemma":0.01637365,"domain_scores_codex":[0.9974003,0.0009652251,0.0002997463,0.0008244033,0.0003661262,0.0001442477],"domain_scores_gemma":[0.9849979,0.007810342,0.000810096,0.00358217,0.002442234,0.0003572011],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001418957,0.0009767303,0.2674054,0.0005730465,0.001297709,0.0002684341,0.00113341,0.2400343,0.009184046,0.003097362,0.00737943,0.4672313],"study_design_scores_gemma":[0.00004945033,0.00009933108,0.02843051,0.00003563653,0.00008123946,0.0001765374,0.0002198202,0.9638028,0.003167924,0.001747634,0.002156279,0.00003280349],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5338343,0.0005941268,0.4563503,0.0004628817,0.000243975,0.000225397,0.003185052,0.004407267,0.0006968162],"genre_scores_gemma":[0.757795,0.0001591661,0.2352615,0.00005376276,0.00003343814,0.0001088956,0.005765879,0.0002057705,0.0006166183],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02025574,"threshold_uncertainty_score":0.04027569,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.232109061360323,"score_gpt":0.4669632635970006,"score_spread":0.2348542022366776,"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."}}