{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004460822,0.0005613659,0.001035343,0.007719564,0.0009216961,0.006105904,0.001256546,0.0009104182,0.004080482],"category_scores_gemma":[0.02825532,0.0005997444,0.0006093769,0.01107412,0.000606586,0.01112464,0.003103419,0.0005912436,0.001254362],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009152496,"about_ca_system_score_gemma":0.001340614,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006981727,"about_ca_topic_score_gemma":0.004095506,"domain_scores_codex":[0.9955206,0.001616298,0.0006157284,0.0004138197,0.0016064,0.0002271518],"domain_scores_gemma":[0.9878099,0.004703135,0.0009289961,0.003193444,0.003115122,0.0002494317],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00078139,0.0003991528,0.02851056,0.001316814,0.0001623214,0.0008879906,0.003079704,0.02876063,0.008888952,0.0767535,0.02097199,0.829487],"study_design_scores_gemma":[0.0002576877,0.0006689124,0.05194005,0.001814739,0.001075866,0.003628506,0.007918904,0.2654017,0.08486369,0.1888203,0.3931816,0.0004281133],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1977745,0.006579537,0.7418408,0.002956731,0.0002708278,0.0006033619,0.007497768,0.008092243,0.03438436],"genre_scores_gemma":[0.679574,0.004559934,0.3023027,0.0002722397,0.0001511016,0.000209512,0.007406251,0.000315931,0.00520837],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.007719564,"threshold_uncertainty_score":0.0235914,"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."}}