{"id":"W2015566532","doi":"10.1016/j.compbiomed.2008.12.006","title":"Dynamic photorefraction system: An offline application for the dynamic analysis of ocular focus and pupil size from photorefraction images","year":2009,"lang":"en","type":"article","venue":"Computers in Biology and Medicine","topic":"Ophthalmology and Visual Impairment Studies","field":"Medicine","cited_by":14,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Accommodation; Focus (optics); Pupil; Artificial intelligence; Computer vision; Pupil size; Vergence (optics); Component (thermodynamics); Component Object Model; Vision science; Computer graphics (images); Optics; Software","routes":{"ca_aff":true,"ca_fund":true,"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":[],"consensus_categories":[],"category_scores_codex":[0.0003951989,0.0001396155,0.0005568056,0.0001926113,0.0001124102,0.000002187805,0.00005436227,0.0001467733,0.000004680237],"category_scores_gemma":[0.00008848481,0.0000880652,0.00004612344,0.0002385376,0.0002708831,0.00005171529,0.00001799861,0.0001322576,1.724566e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005481646,"about_ca_system_score_gemma":0.000009096129,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005183097,"about_ca_topic_score_gemma":0.00006076952,"domain_scores_codex":[0.9991123,0.00007706672,0.0003105295,0.0003080243,0.00005654845,0.0001355201],"domain_scores_gemma":[0.9988049,0.0007069014,0.0001763663,0.0002000676,0.00006189129,0.00004987546],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.005074201,0.001271546,0.5016549,0.0004339134,0.004260109,0.00002380197,0.0023105,0.00004959812,0.3001125,0.0006542419,0.0001307953,0.1840239],"study_design_scores_gemma":[0.002078714,0.002742159,0.9174185,0.0001060936,0.001676509,0.00003030809,0.0006533671,0.07416903,0.0002365371,0.0007543361,0.00005268189,0.00008180161],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9676305,0.003076038,0.02661988,0.001797702,0.0002042513,0.0006066803,0.00002108182,0.00002652466,0.00001734596],"genre_scores_gemma":[0.9974344,0.0008968986,0.001011718,0.0003024362,0.000066625,0.00003825005,0.0002342179,0.000004984543,0.00001047494],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4157636,"threshold_uncertainty_score":0.3591194,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01284049598255692,"score_gpt":0.3719227869225019,"score_spread":0.359082290939945,"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."}}