{"id":"W3022230630","doi":"","title":"Analysis of Vehicle Ownership Evolution in Montreal, Canada Using Pseudo Panel Analysis","year":2014,"lang":"en","type":"article","venue":"Journal of International Crisis and Risk Communication Research","topic":"Urban Transport and Accessibility","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Car ownership; License; Nested logit; Econometrics; Econometric model; Logit; Panel data; Ordered logit; Econometric analysis; Binary logit model; Demographic economics; Economics; Business; Statistics; Computer science; Mathematics; Transport engineering; Engineering; Public transport","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.0008704495,0.0002891176,0.0003036593,0.001727355,0.0008781115,0.0009021874,0.0008837453,0.0002234534,0.004412583],"category_scores_gemma":[0.002543954,0.0002184757,0.0004706328,0.005105907,0.0002557298,0.000299485,0.0005260382,0.0003825238,0.00037163],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01173777,"about_ca_system_score_gemma":0.01151468,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9933079,"about_ca_topic_score_gemma":0.9950324,"domain_scores_codex":[0.9993582,0.0001359348,0.00002322528,0.0001195404,0.0001978029,0.0001653257],"domain_scores_gemma":[0.9976909,0.0002669689,0.000271454,0.0001825095,0.001383921,0.0002042801],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001099763,0.00004703787,0.9564773,0.00006599732,0.0002524359,0.0001530525,0.0006960108,0.007713324,0.0005989447,0.001887816,0.0136933,0.01830489],"study_design_scores_gemma":[0.000005485233,0.00001431038,0.9839587,0.00001589554,0.00002524538,0.00001939855,0.0005027417,0.00832971,0.0001845316,0.00007220859,0.006855257,0.00001654423],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8872012,0.0007428181,0.005562005,0.0004800595,0.00002446858,0.0002010459,0.09914381,0.0001452779,0.006499363],"genre_scores_gemma":[0.9333928,0.0004185817,0.00395888,0.0001140249,0.00001240473,0.0001797508,0.05553497,0.00002985072,0.006358923],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01173777,"threshold_uncertainty_score":0.08516383,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08258136955011956,"score_gpt":0.3993498228433757,"score_spread":0.3167684532932561,"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."}}