{"id":"W2147201860","doi":"10.3138/carto.46.2.101","title":"Improving Accessibility Information in Pedestrian Maps and Databases","year":2011,"lang":"en","type":"article","venue":"Cartographica The International Journal for Geographic Information and Geovisualization","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":23,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"FP7 Information and Communication Technologies","keywords":"Geospatial analysis; Pedestrian; Computer science; Stairs; Database; Spatial database; Spatial analysis; Information retrieval; Geographic information system; World Wide Web; Data science; Geography; Transport engineering; Cartography; Engineering; Remote sensing","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001625379,0.0001606952,0.0001506746,0.001142455,0.0003581222,0.00102683,0.0005526224,0.00007400964,0.00000920789],"category_scores_gemma":[0.0003921174,0.0001278123,0.00008923611,0.0005525774,0.0001024596,0.01064509,0.0002049541,0.0001986808,0.000002588002],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003066062,"about_ca_system_score_gemma":0.00007990931,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004481901,"about_ca_topic_score_gemma":0.000202139,"domain_scores_codex":[0.9983267,0.000104262,0.0007764593,0.000151982,0.0004344147,0.0002062331],"domain_scores_gemma":[0.99808,0.0001627382,0.0006129781,0.0002104982,0.0008329463,0.000100802],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0003362791,0.0001427633,0.2402539,0.0002053946,0.0001961537,0.000004097617,0.01490523,0.00003034051,0.0000627224,0.1071663,0.0007484506,0.6359484],"study_design_scores_gemma":[0.0102833,0.0006305991,0.5645869,0.0005101236,0.0001263001,0.001585031,0.009528516,0.1983351,0.0006584963,0.05326296,0.1588958,0.001596896],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2276733,0.0001887158,0.7678748,0.001182387,0.001593648,0.0008224014,0.00008637022,0.0001068714,0.0004715179],"genre_scores_gemma":[0.9969965,0.0003911869,0.001460114,0.0008633243,0.00006424863,0.0000641651,0.0001514936,0.000004501389,0.000004448907],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7693233,"threshold_uncertainty_score":0.9901744,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03734314760404615,"score_gpt":0.2916328064219754,"score_spread":0.2542896588179292,"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."}}