{"id":"W2979733112","doi":"10.1080/03081060.2019.1675321","title":"GIS-based transit trip allocation methods converting stop-level boarding and alighting trips into TAZ trips","year":2019,"lang":"en","type":"article","venue":"Transportation Planning and Technology","topic":"Urban Transport and Accessibility","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"TRIPS architecture; Transport engineering; Transit (satellite); Computer science; Public transport; Engineering","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005247546,0.0007400305,0.0005380951,0.002065971,0.0002576809,0.0006778829,0.0007284901,0.0003341461,0.002948344],"category_scores_gemma":[0.002620543,0.0002920876,0.0004415328,0.002052755,0.0002034537,0.0008478792,0.0005326487,0.0004918303,0.001010548],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004688585,"about_ca_system_score_gemma":0.0008796955,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009901588,"about_ca_topic_score_gemma":0.009092037,"domain_scores_codex":[0.9996504,0.00009770177,0.00002931299,0.00009677407,0.0000975958,0.00002827414],"domain_scores_gemma":[0.9992558,0.0002257493,0.00009776283,0.00009711867,0.0003001499,0.00002331252],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001596553,0.0001588971,0.005574882,0.0001538514,0.00008914254,0.00003965724,0.0001761058,0.3686266,0.004481527,0.003784277,0.003024422,0.613731],"study_design_scores_gemma":[0.00001087385,0.00002966195,0.00290066,0.00000878384,0.00001110385,0.00003405053,0.00008645783,0.9909824,0.001898778,0.001910924,0.002110738,0.00001569992],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.03884904,0.00009683659,0.9568177,0.00007464035,0.00006645377,0.000130969,0.0006040253,0.001421178,0.001939029],"genre_scores_gemma":[0.399947,0.0001734181,0.5951468,0.00003970131,0.00006448757,0.000346447,0.001580155,0.0001351231,0.002566917],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.009901588,"threshold_uncertainty_score":0.01968789,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03381816516095198,"score_gpt":0.352525553319223,"score_spread":0.318707388158271,"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."}}