{"id":"W2062433653","doi":"10.1080/715020598","title":"Impact of telecommuting and intelligent transportation systems on residential location choice","year":2003,"lang":"en","type":"article","venue":"Transportation Planning and Technology","topic":"Urban Transport and Accessibility","field":"Social Sciences","cited_by":50,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"Science and Engineering Research Board; Natural Sciences and Engineering Research Council of Canada","keywords":"Telecommuting; Mixed logit; Discrete choice; Logit; Preference; Transport engineering; Transportation planning; Estimation; Choice modelling; Urban planning; Public transport; Computer science; Business; Logistic regression; Econometrics; Economics; Engineering; Marketing; Microeconomics; Civil engineering","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003317032,0.0001094467,0.000188178,0.0002353607,0.0002371329,0.00002251105,0.00007262815,0.0001852246,0.00001623316],"category_scores_gemma":[0.00006451152,0.0001058739,0.00003083204,0.0004732585,0.0002333606,0.0001492472,4.13053e-7,0.0001558987,6.187226e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002797233,"about_ca_system_score_gemma":0.0000838258,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002834691,"about_ca_topic_score_gemma":0.001678675,"domain_scores_codex":[0.9990327,0.00005162527,0.0003489989,0.0002257686,0.0001560321,0.0001849018],"domain_scores_gemma":[0.9994496,0.00009746521,0.0001722186,0.0001022926,0.0001239128,0.00005449728],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00002546206,0.00004306256,0.9785208,0.0000538241,0.00002524103,0.000002932796,0.003800495,0.0005206637,0.0002994982,0.01540546,0.00001022226,0.001292402],"study_design_scores_gemma":[0.0003270395,0.000150702,0.9941938,0.0001166971,0.00004522778,4.056455e-7,0.002745174,0.00004238425,0.0008521201,0.00105835,0.0003391761,0.0001289445],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.996358,0.0008047679,0.001981828,0.000081295,0.00008014504,0.0002159176,0.00001649457,0.0001048535,0.0003566933],"genre_scores_gemma":[0.9996937,0.00005507951,0.0001198669,0.000004338905,0.00001635613,0.00001139101,0.00005116229,0.000008037126,0.0000400776],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01567304,"threshold_uncertainty_score":0.4317412,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02372102902478775,"score_gpt":0.3317951124801324,"score_spread":0.3080740834553446,"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."}}