{"id":"W2155429018","doi":"10.1016/j.clae.2014.11.008","title":"Upper lid margin staining with different soft contact lenses and lens care solution combinations","year":2015,"lang":"en","type":"article","venue":"Contact Lens and Anterior Eye","topic":"Ocular Surface and Contact Lens","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Margin (machine learning); Contact lens; Staining; Lens (geology); Materials science; Optics; Biomedical engineering; Ophthalmology; Computer science; Medicine; Physics; Pathology","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":[],"consensus_categories":[],"category_scores_codex":[0.0000996981,0.0003162792,0.0005589358,0.0001257925,0.0002091887,0.0001069902,0.00005414373,0.000118339,0.00002340624],"category_scores_gemma":[0.00004154393,0.0002354158,0.00008414222,0.00007536208,0.00007620155,0.000306626,0.00007129095,0.0002414011,0.000007159281],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001013669,"about_ca_system_score_gemma":0.0001262128,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000167619,"about_ca_topic_score_gemma":0.0002163528,"domain_scores_codex":[0.9986374,0.00005789187,0.0002633457,0.0003929797,0.0002493332,0.0003990452],"domain_scores_gemma":[0.9990907,0.00003992571,0.0001047488,0.0002678885,0.0002516338,0.0002451475],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000697758,0.000185278,0.9656511,0.0002738615,0.0001725032,0.0002088466,0.004611274,2.695134e-7,0.01821467,0.0007393962,0.0000485969,0.009196443],"study_design_scores_gemma":[0.01144195,0.005347459,0.9513998,0.001943819,0.0004410978,0.0001110523,0.01327824,0.0002905834,0.001903536,0.00002227369,0.01319605,0.0006241346],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9918832,0.004856084,0.0003101824,0.001164867,0.00013931,0.0004573072,0.00003859672,0.00009360264,0.001056915],"genre_scores_gemma":[0.9980547,0.0006549383,0.00007954285,0.0005463707,0.00007861434,0.00002081743,0.00004621537,0.00004208838,0.0004766435],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01631114,"threshold_uncertainty_score":0.9599978,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01995881155773016,"score_gpt":0.257364567216527,"score_spread":0.2374057556587968,"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."}}