{"id":"W3042473914","doi":"10.3390/ijgi9070456","title":"A Deep Learning Approach to Urban Street Functionality Prediction Based on Centrality Measures and Stacked Denoising Autoencoder","year":2020,"lang":"en","type":"article","venue":"ISPRS International Journal of Geo-Information","topic":"Urban Design and Spatial Analysis","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Centrality; Autoencoder; Transport engineering; Computer science; Street network; Classifier (UML); Artificial intelligence; Geography; Deep learning; Engineering; Mathematics","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":[0.0005085015,0.0007883824,0.0006474359,0.0007914787,0.0002374695,0.0006130163,0.0009234692,0.0007730434,0.001052927],"category_scores_gemma":[0.001029341,0.0003941582,0.0007807505,0.0007093753,0.0003824432,0.0008308725,0.0006347186,0.001223614,0.0004118646],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005976537,"about_ca_system_score_gemma":0.0005274412,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009953644,"about_ca_topic_score_gemma":0.01063325,"domain_scores_codex":[0.9997725,0.00003986532,0.00001498284,0.00007508565,0.00004732315,0.00005023854],"domain_scores_gemma":[0.9996729,0.0001252644,0.0000351934,0.00002456316,0.0001226757,0.0000195677],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001528524,0.0002235029,0.005042625,0.00007132124,0.0001403439,0.0001740679,0.0001248076,0.6851112,0.01040757,0.003422363,0.002813724,0.2923156],"study_design_scores_gemma":[0.000001517676,0.00001350656,0.0003623118,0.000003715972,0.000006738761,0.000008340263,0.000007759388,0.9982224,0.0006127628,0.0006135528,0.0001445057,0.000002877023],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09661193,0.0006899048,0.8982843,0.0004195961,0.00009424141,0.00004385785,0.0002371917,0.001291475,0.002327483],"genre_scores_gemma":[0.8720069,0.0005078903,0.1212125,0.0002076192,0.00008653032,0.00008592574,0.0009402139,0.00007666732,0.004875735],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009953644,"threshold_uncertainty_score":0.01979142,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01265162829631089,"score_gpt":0.1984702434994482,"score_spread":0.1858186152031373,"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."}}