{"id":"W2888971253","doi":"10.1097/opx.0000000000001269","title":"Effectiveness of the Apple iPad as a Spot‐reading Magnifier","year":2018,"lang":"en","type":"article","venue":"Optometry and Vision Science","topic":"Assistive Technology in Communication and Mobility","field":"Health Professions","cited_by":35,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University; Centre Intégré Universitaire de Santé et de Services Sociaux du Centre-Sud-de-l'Île-de-Montréal; Centre intégré de santé et de services sociaux de la Montérégie-Centre; Centre Intégré Universitaire de Santé et de Services Sociaux du Saguenay–Lac-Saint-Jean; Centre intégré de santé et de services sociaux de Chaudière-Appalaches; Centre Intégré de Santé et de Services Sociaux des Laurentides; Université de Montréal; Santé Montérégie; MAB-Mackay Rehabilitation Centre","funders":"","keywords":"Magnification; Computer science; Reading (process); Assistive technology; Usability; Multimedia; Optometry; Human–computer interaction; Medicine; Computer vision","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.004277716,0.0000636777,0.0001242354,0.0001466753,0.001533942,0.000007759061,0.0005897993,0.00009258959,0.0001663479],"category_scores_gemma":[0.001419926,0.00003904265,0.00002327319,0.001543973,0.002608008,0.0001408693,0.0007655997,0.0002852211,0.00007402557],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004777641,"about_ca_system_score_gemma":0.0001366849,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002706517,"about_ca_topic_score_gemma":0.000001383836,"domain_scores_codex":[0.9987477,0.0003884397,0.0001856327,0.0002448408,0.0002303948,0.0002029695],"domain_scores_gemma":[0.9978663,0.0009706921,0.0001305577,0.0006715521,0.0003033792,0.0000575581],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001126488,0.00009277558,0.8611553,0.0001827881,0.00000484951,1.682019e-7,0.0007142818,1.553944e-7,0.1107956,0.01457483,0.0002328025,0.01213384],"study_design_scores_gemma":[0.0002573091,0.0000935213,0.9676999,0.0002278905,0.000004171339,0.000001340319,0.0008132062,0.00009067442,0.02622599,0.001006712,0.003525727,0.00005356505],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9777403,0.00003281634,0.001244693,0.0003155513,0.0003011892,0.0003354365,0.000004083059,0.00003279212,0.01999316],"genre_scores_gemma":[0.9988056,0.00001364063,0.0005789557,0.0001489346,0.0000147248,0.00002385311,3.4694e-7,0.000002977539,0.0004110095],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1065446,"threshold_uncertainty_score":0.9997659,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04101576360530602,"score_gpt":0.5419642187860507,"score_spread":0.5009484551807447,"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."}}