{"id":"W2701595384","doi":"","title":"Mining Sequential Association Rules for Traveler Context Prediction","year":2010,"lang":"en","type":"article","venue":"","topic":"Human Mobility and Location-Based Analysis","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Wilfrid Laurier University","funders":"","keywords":"Computer science; Association rule learning; Context (archaeology); Global Positioning System; Data mining; Work (physics); Similarity (geometry); Data science; Operations research; Machine learning; Artificial intelligence; Engineering; Geography","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001274805,0.00004552164,0.00008203463,0.00004479346,0.0005631861,0.00008710097,0.00007961509,0.0001305013,0.001811908],"category_scores_gemma":[0.0006585639,0.00004542397,0.00008687391,0.00008756047,0.00005925069,0.0001452583,0.00000381855,0.00005720253,0.0000363945],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001001231,"about_ca_system_score_gemma":0.000141223,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004114483,"about_ca_topic_score_gemma":0.09966651,"domain_scores_codex":[0.9992664,0.00007071296,0.0001505002,0.0001354187,0.0002186552,0.0001582654],"domain_scores_gemma":[0.9993315,0.0002631572,0.00007509496,0.000073395,0.0002017777,0.00005506635],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00007345335,0.0004716805,0.1439644,0.00004677136,0.0004223308,4.761048e-7,0.08250279,0.00009635316,0.02069374,0.3566848,0.04542875,0.3496144],"study_design_scores_gemma":[0.001979341,0.0001436323,0.05241519,0.00002154263,0.0004321656,2.430143e-7,0.0535746,0.02123145,0.006860476,0.01561882,0.8471003,0.0006222707],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9414864,0.000006495212,0.02658472,0.002921179,0.0006869579,0.0003322883,0.00003369802,0.0001355285,0.02781275],"genre_scores_gemma":[0.9881399,0.000002787091,0.0006439172,0.0001718188,0.0006367304,0.00004038098,0.00005128024,0.000003958686,0.01030918],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8016715,"threshold_uncertainty_score":0.9991006,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02500126981451124,"score_gpt":0.3041500976781117,"score_spread":0.2791488278636005,"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."}}