{"id":"W3096102068","doi":"10.3390/ijgi9110649","title":"Personalized Legibility of an Indoor Environment for People with Motor Disabilities: A New Framework","year":2020,"lang":"en","type":"article","venue":"ISPRS International Journal of Geo-Information","topic":"Tactile and Sensory Interactions","field":"Neuroscience","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval; Centre for Interdisciplinary Research in Rehabilitation","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Legibility; Perception; Visibility; Computer science; Personalization; Human–computer interaction; Psychology; Geography; World Wide Web; Business; Advertising","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":[0.001827792,0.001222264,0.0004818653,0.004648107,0.001927673,0.005866714,0.001360732,0.001136242,0.003664513],"category_scores_gemma":[0.00651435,0.0004017079,0.000917012,0.002201773,0.004852842,0.005297259,0.005324075,0.001362429,0.0004843973],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.005381208,"about_ca_system_score_gemma":0.002986229,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0477451,"about_ca_topic_score_gemma":0.06358603,"domain_scores_codex":[0.9983266,0.0006003747,0.0001044997,0.0003560259,0.0004470371,0.0001654962],"domain_scores_gemma":[0.9955075,0.001751406,0.0005661586,0.0003348138,0.001376861,0.0004631889],"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":[0.000392868,0.001190358,0.2226622,0.002755033,0.0002963448,0.002648372,0.1013771,0.02430167,0.01022398,0.1671391,0.009737539,0.4572754],"study_design_scores_gemma":[0.00006192179,0.001026489,0.4501467,0.002746492,0.0007350874,0.002375316,0.1282284,0.1643448,0.006314653,0.1094434,0.1339235,0.000653245],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5430261,0.006981042,0.3230714,0.006926157,0.0002692282,0.001029442,0.001328508,0.0007769281,0.1165912],"genre_scores_gemma":[0.9247021,0.00144862,0.06948393,0.0001594578,0.00005085892,0.0002201717,0.0003303864,0.00006488011,0.003539585],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0477451,"threshold_uncertainty_score":0.0949344,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02661133344197138,"score_gpt":0.2880912686129848,"score_spread":0.2614799351710134,"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."}}