{"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":"codex-gemma-dda1882f352a","candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.000922318,0.0002001658,0.0002450495,0.0001935652,0.001391431,0.00007991568,0.0001634747,0.0001823154,0.0004836571],"category_scores_gemma":[0.00004278506,0.0002070349,0.0001643561,0.0003373809,0.000644048,0.00008954979,0.000001871894,0.0005089688,0.00005995149],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002631441,"about_ca_system_score_gemma":0.0002392077,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001524888,"about_ca_topic_score_gemma":0.01258803,"domain_scores_codex":[0.9978501,0.0005917759,0.000359736,0.0005138544,0.0003504976,0.0003340866],"domain_scores_gemma":[0.9983894,0.0009463283,0.00008700944,0.0002759263,0.0001392905,0.000162021],"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":[0.00007171087,0.0006858265,0.000146151,0.00001545123,0.00002840813,7.385361e-7,0.01390056,0.4954812,0.00007263164,0.0001329336,0.00006616745,0.4893982],"study_design_scores_gemma":[0.002241086,0.0009905389,0.001254079,0.0001750846,0.0001694316,0.000002394286,0.007978141,0.9594303,0.002026176,0.0001889578,0.0249202,0.0006235731],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2845886,0.00004663844,0.7122755,0.0002537689,0.0002829645,0.0005737967,0.00007026038,0.0002293981,0.001679058],"genre_scores_gemma":[0.9986923,0.000007455563,0.0002828099,0.0002567809,0.0004587797,0.00006724349,0.0000129285,0.00001504506,0.0002066571],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7141037,"threshold_uncertainty_score":0.9999086,"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."}}