{"id":"W3128795561","doi":"10.5198/jtlu.2021.1808","title":"needs-gap analysis of street space allocation","year":2021,"lang":"en","type":"article","venue":"Journal of Transport and Land Use","topic":"Urban Transport and Accessibility","field":"Social Sciences","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal; Polytechnique Montréal","funders":"Fonds de recherche du Québec – Nature et technologies; Ministère des Transports","keywords":"Borough; TRIPS architecture; Transport engineering; Equity (law); Space (punctuation); Computer science; Business; Geography; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002177098,0.0003506052,0.0004017864,0.004601025,0.0004590728,0.001001895,0.0007274304,0.0004505361,0.002662623],"category_scores_gemma":[0.008489136,0.0001762993,0.0005927546,0.004323278,0.0004781284,0.001091653,0.001277136,0.0003387079,0.0002448691],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002803984,"about_ca_system_score_gemma":0.001665433,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.08787,"about_ca_topic_score_gemma":0.06159537,"domain_scores_codex":[0.9987388,0.0004122735,0.00007014483,0.0001971678,0.0003647681,0.000216789],"domain_scores_gemma":[0.996182,0.001932537,0.0005434302,0.00028895,0.0009058448,0.0001472048],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0004091839,0.0001959897,0.7585536,0.0002308685,0.0002666319,0.000251918,0.002711806,0.1508038,0.001430847,0.02147353,0.004661028,0.05901072],"study_design_scores_gemma":[0.00001377368,0.0001345929,0.4806051,0.00005675717,0.00004945187,0.0001576421,0.007332049,0.4904369,0.001384425,0.01186934,0.007925345,0.00003454467],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9613187,0.0001688411,0.02554885,0.0002335007,0.000007107742,0.0001190327,0.006821738,0.0001437525,0.005638563],"genre_scores_gemma":[0.986986,0.00004429528,0.009042942,0.00001518316,0.000004082411,0.00009303104,0.003175108,0.00001525119,0.0006240126],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.08787,"threshold_uncertainty_score":0.1747171,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03217030822992198,"score_gpt":0.2927892756140064,"score_spread":0.2606189673840844,"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."}}