{"id":"W3033877789","doi":"10.1080/17483107.2020.1768307","title":"Evaluation of satisfaction with geospatial assistive technology (ESGAT): a methodological and usability study","year":2020,"lang":"en","type":"article","venue":"Disability and Rehabilitation Assistive Technology","topic":"Assistive Technology in Communication and Mobility","field":"Health Professions","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Centre intégré universitaire de santé et de services sociaux de la Capitale-Nationale; Centre for Interdisciplinary Research in Rehabilitation; Centre Intégré Universitaire de Santé et de Services Sociaux du Centre-Sud-de-l'Île-de-Montréal; Université Laval; McGill University; Centre Intégré Universitaire de Santé et de Services Sociaux du Saguenay–Lac-Saint-Jean","funders":"","keywords":"Usability; Geospatial analysis; Wheelchair; Pedestrian; Transport engineering; Computer science; Applied psychology; Psychology; Engineering; Human–computer interaction; World Wide Web; Geography; Cartography","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":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.007457304,0.0003938528,0.001050592,0.0003071301,0.0008180317,0.000008330253,0.000303616,0.0009630864,0.0001936417],"category_scores_gemma":[0.02429207,0.0003136938,0.00008043971,0.001491836,0.007131482,0.0001905001,0.0005834837,0.001494501,0.000008787041],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005081368,"about_ca_system_score_gemma":0.0003345233,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003226315,"about_ca_topic_score_gemma":0.0022698,"domain_scores_codex":[0.9907834,0.005561939,0.00126697,0.001293755,0.0006640729,0.0004298711],"domain_scores_gemma":[0.9908047,0.005017636,0.0008148167,0.001044416,0.002157248,0.0001611556],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0005437197,0.0007013574,0.9097568,0.0001667484,0.0001423661,4.957396e-7,0.0008279879,0.000006822093,0.000413209,0.002686539,0.00002207855,0.08473188],"study_design_scores_gemma":[0.002496964,0.0034357,0.9483115,0.00006459731,0.0003842968,0.000003054226,0.03982605,0.0008837244,0.00005222763,0.004145276,0.0001236889,0.0002729072],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9664448,0.0001873307,0.004119416,0.02376886,0.00007971528,0.004435579,0.00006717002,0.0005746663,0.0003224445],"genre_scores_gemma":[0.9831901,0.00001879634,0.01489892,0.0001165101,0.00002384924,0.001711523,0.0000136708,0.00002212981,0.000004456735],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.08445898,"threshold_uncertainty_score":0.9999315,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1460145737453523,"score_gpt":0.4675518793540531,"score_spread":0.3215373056087008,"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."}}