{"id":"W2020766884","doi":"10.1186/1476-072x-12-14","title":"Detecting activity locations from raw GPS data: a novel kernel-based algorithm","year":2013,"lang":"en","type":"article","venue":"International Journal of Health Geographics","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":132,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"Fonds de Recherche du Québec - Santé","keywords":"Global Positioning System; Computer science; Algorithm; Context (archaeology); Noise (video); Kernel (algebra); Data mining; Artificial intelligence; Mathematics; Geography","routes":{"ca_aff":true,"ca_fund":true,"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":[],"consensus_categories":[],"category_scores_codex":[0.0009947979,0.0001682843,0.000408499,0.0004715628,0.0001168556,0.00009553821,0.0007437357,0.00007678344,0.0001736819],"category_scores_gemma":[0.0005929199,0.0001531565,0.0001667745,0.0002945634,0.0001105506,0.0005891222,0.0001494488,0.0005762111,0.00003421414],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001734296,"about_ca_system_score_gemma":0.001120487,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002421608,"about_ca_topic_score_gemma":0.0001630054,"domain_scores_codex":[0.9974769,0.000105581,0.0008193913,0.0002878296,0.001033959,0.0002763759],"domain_scores_gemma":[0.9961846,0.0004121898,0.001035151,0.0005719694,0.001437956,0.0003581411],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0002968955,0.002012053,0.1320222,0.00008756428,0.001169784,0.0001023655,0.0001305678,0.0002839975,0.001296256,0.00007007181,0.006047058,0.8564812],"study_design_scores_gemma":[0.004980367,0.000363243,0.8772696,0.0007090282,0.0001075567,0.0002525705,0.0001583737,0.09602349,0.0002474872,0.0006113596,0.01900225,0.000274687],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2770417,0.002254572,0.6936013,0.02270128,0.002396111,0.0005541433,0.001275057,0.00008647037,0.00008935411],"genre_scores_gemma":[0.9520974,0.0003205531,0.04321665,0.003016277,0.0007687945,0.000009261484,0.000534687,0.00002752671,0.000008851225],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8562065,"threshold_uncertainty_score":0.624554,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04901334359892456,"score_gpt":0.3578677350127343,"score_spread":0.3088543914138098,"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."}}