{"id":"W2134537668","doi":"10.1109/tmc.2005.74","title":"A mobility prediction architecture based on contextual knowledge and spatial conceptual maps","year":2005,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Human Mobility and Location-Based Analysis","field":"Social Sciences","cited_by":134,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science; Component (thermodynamics); Context (archaeology); A priori and a posteriori; Process (computing); Data mining; Artificial intelligence; Dempster–Shafer theory; Information retrieval","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":[0.0006367369,0.0004588628,0.0005039469,0.0009953257,0.0007598777,0.001183142,0.001667794,0.000622685,0.001549602],"category_scores_gemma":[0.001808327,0.0003368086,0.000505852,0.0009357811,0.0005670208,0.002687691,0.001350865,0.0007842063,0.0004635358],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007893517,"about_ca_system_score_gemma":0.001497708,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02256063,"about_ca_topic_score_gemma":0.02487513,"domain_scores_codex":[0.9996853,0.00004457628,0.00002626158,0.0001199159,0.0000851126,0.00003890156],"domain_scores_gemma":[0.9994881,0.0001243024,0.00005512931,0.00009013884,0.0001923727,0.0000498575],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003377175,0.0002896184,0.01346788,0.0002001116,0.0002280471,0.0004599738,0.0008028055,0.488733,0.007151746,0.07002599,0.00902421,0.409279],"study_design_scores_gemma":[0.000007630922,0.00002993499,0.001057549,0.00001327392,0.00005899715,0.00006830703,0.00005416011,0.9814803,0.00142745,0.01239259,0.003388799,0.00002100561],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0245772,0.0002294483,0.968465,0.0004290053,0.00004058742,0.00006593645,0.0002486546,0.003360253,0.002583988],"genre_scores_gemma":[0.6423408,0.0004466368,0.3529204,0.000110908,0.00005350568,0.0001433345,0.001013684,0.00007661061,0.002893886],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02256063,"threshold_uncertainty_score":0.04485863,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01696949112275252,"score_gpt":0.2824695090405268,"score_spread":0.2655000179177743,"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."}}