{"id":"W2913122395","doi":"10.1139/geomat-2018-0006","title":"Pedestrian network information extraction based on VGI","year":2018,"lang":"en","type":"article","venue":"GEOMATICA","topic":"Geographic Information Systems Studies","field":"Social Sciences","cited_by":4,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Pedestrian; Computer science; Transport engineering; Volunteered geographic information; Global Positioning System; Data mining; Data science; Engineering; Telecommunications","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002107912,0.001581849,0.0009244978,0.01537285,0.000839922,0.00136685,0.000776438,0.0008084868,0.009159788],"category_scores_gemma":[0.0009885333,0.0005044778,0.001224463,0.01036492,0.0002139033,0.001411388,0.0013174,0.0005143772,0.009035449],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006530558,"about_ca_system_score_gemma":0.001241276,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02008926,"about_ca_topic_score_gemma":0.02697284,"domain_scores_codex":[0.9995876,0.00002152223,0.00002849862,0.0001339384,0.0001376051,0.0000908825],"domain_scores_gemma":[0.9996926,0.00003195574,0.00003234254,0.00006848346,0.0001511336,0.00002363858],"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":[0.0002714602,0.0002236077,0.02291497,0.0009307406,0.0002388713,0.0009822957,0.0003636365,0.00725703,0.03264673,0.005044208,0.1095623,0.8195641],"study_design_scores_gemma":[0.00007645695,0.0002556969,0.1746615,0.0008765448,0.001247463,0.003919301,0.00207905,0.3512721,0.09656725,0.02268165,0.3460508,0.00031226],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1284957,0.003561956,0.5465408,0.0006128497,0.0007369383,0.001835573,0.1911599,0.04749416,0.07956211],"genre_scores_gemma":[0.3807345,0.002787144,0.380636,0.0002001392,0.0002400737,0.0009533179,0.2104007,0.001243979,0.02280422],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02008926,"threshold_uncertainty_score":0.03994465,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01676650900220488,"score_gpt":0.3018858870187472,"score_spread":0.2851193780165424,"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."}}