{"id":"W4390445476","doi":"10.1016/j.jtrangeo.2023.103777","title":"Assessment of freight accessibility in New York City: A spatial-temporal approach","year":2023,"lang":"en","type":"article","venue":"Journal of Transport Geography","topic":"Urban and Freight Transport Logistics","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":false,"ca_institutions":"Canada Research Chairs; University of Toronto","funders":"","keywords":"Metropolitan area; Traffic management; Context (archaeology); Transport engineering; Externality; Business; Land use; City logistics; Vehicle miles of travel; Greenhouse gas; Truck; Traffic congestion; Economics; Geography; Engineering; Civil 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006434859,0.0002318408,0.0006477622,0.0008752276,0.00002364396,0.00001292734,0.0003778371,0.0001563894,0.00009753961],"category_scores_gemma":[0.000003913541,0.0002046826,0.000476284,0.001152315,0.00008833493,0.0001666183,0.000005241809,0.000520213,8.825804e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003322157,"about_ca_system_score_gemma":0.0001549892,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003226355,"about_ca_topic_score_gemma":0.0003563887,"domain_scores_codex":[0.997772,0.00002364598,0.001177983,0.0001764289,0.0005210484,0.000328826],"domain_scores_gemma":[0.9992046,0.00003792259,0.0002161587,0.0002633322,0.000074806,0.0002032086],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00003070834,0.0001677177,0.9893092,0.0002328078,0.0001536507,0.0001198525,0.0002259186,0.008179607,0.0001908369,0.0001197852,0.0003496126,0.0009203221],"study_design_scores_gemma":[0.0009678742,0.00009739994,0.9929572,0.00009216648,0.0001016066,0.000007556888,0.00005138484,0.003315735,0.0001741943,0.0008978188,0.001140142,0.0001969559],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8334637,0.0008962388,0.1594914,0.0000354374,0.0006520734,0.0002288814,0.0000697412,0.0001251312,0.005037393],"genre_scores_gemma":[0.9896345,0.0002757981,0.009820505,0.0000113453,0.0001445339,0.000002603995,0.00005635451,0.00003103362,0.00002330461],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1561708,"threshold_uncertainty_score":0.8346712,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04030865573828401,"score_gpt":0.2468234800199964,"score_spread":0.2065148242817124,"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."}}