{"id":"W2137065129","doi":"10.1111/j.1538-4632.2000.tb00431.x","title":"Using Decision Tree Induction Systems for Modeling Space‐Time Behavior","year":2000,"lang":"en","type":"article","venue":"Geographical Analysis","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":53,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ministry of Transportation of Ontario","funders":"","keywords":"CHAID; Decision tree; Computer science; Decision rule; Decision tree learning; Incremental decision tree; Decision support system; Machine learning; Heuristic; Decision tree model; Artificial intelligence; Operations research; Data mining; Mathematics","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":[],"consensus_categories":[],"category_scores_codex":[0.0004867658,0.00008699374,0.0002012395,0.0004496666,0.0006322532,0.0001134812,0.00009493234,0.0001571197,0.0002987766],"category_scores_gemma":[0.00003125799,0.00008431165,0.0002876846,0.002250318,0.0000585515,0.0001860038,0.00000197449,0.00007018133,0.000008157964],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002519799,"about_ca_system_score_gemma":0.00002647407,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004490166,"about_ca_topic_score_gemma":0.0009200144,"domain_scores_codex":[0.9988272,0.00007392639,0.0002655485,0.0002606889,0.0003540613,0.0002185393],"domain_scores_gemma":[0.9994926,0.00006764361,0.00005979008,0.0001180067,0.0001530914,0.0001089052],"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":[0.0000289886,0.00004122492,0.01817492,0.00000229254,0.0001043636,8.927913e-7,0.0004216294,0.973591,0.00001402573,0.0003858565,0.00001399393,0.007220779],"study_design_scores_gemma":[0.000183684,0.00001482544,0.006528377,0.00001245044,0.001743003,3.430533e-7,0.0003618538,0.9900577,0.00000108521,0.0002503376,0.0007039467,0.0001423985],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7554429,0.0001069486,0.2436497,0.00009821622,0.00006528961,0.0001773459,0.00001644524,0.00008570968,0.0003574267],"genre_scores_gemma":[0.9909859,0.0002025596,0.008188929,0.00001291886,0.0001187442,0.00002862029,0.0001232079,0.00000781405,0.0003312537],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2355431,"threshold_uncertainty_score":0.6787817,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03724005568645729,"score_gpt":0.3154939277026823,"score_spread":0.278253872016225,"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."}}