{"id":"W2993675976","doi":"","title":"Using corneal chemical detection thresholds to select subjects for ocular surface discrimination sensory panels.","year":2016,"lang":"en","type":"article","venue":"Investigative Ophthalmology & Visual Science","topic":"Ocular and Laser Science Research","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Sensory system; Ophthalmology; Optometry; Audiology; Medicine; Psychology; Neuroscience","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008518225,0.0004240531,0.000235588,0.0005436546,0.0001943236,0.0004209984,0.0001745755,0.0004557875,0.003617699],"category_scores_gemma":[0.002462221,0.0001218572,0.0001542995,0.0002455385,0.0001612502,0.0003477276,0.0002183692,0.0002969344,0.0006659409],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001014417,"about_ca_system_score_gemma":0.0001989992,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001119501,"about_ca_topic_score_gemma":0.002689342,"domain_scores_codex":[0.9997566,0.00009710879,0.00001435837,0.00005702278,0.00004695811,0.00002800592],"domain_scores_gemma":[0.9990896,0.0005281224,0.00007028077,0.00004815062,0.0001600646,0.0001037952],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.01491462,0.002069912,0.1888873,0.000354012,0.0001652497,0.0003792264,0.0004662755,0.0009356997,0.6438964,0.000419378,0.002898786,0.1446131],"study_design_scores_gemma":[0.0005680749,0.009304808,0.8524176,0.00007250763,0.0002606652,0.001586511,0.00068418,0.009259869,0.1217992,0.0007623327,0.003200467,0.000083698],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9833252,0.0007468892,0.01017583,0.0001592491,0.00006835761,0.0002568938,0.0007160354,0.0001685945,0.004382868],"genre_scores_gemma":[0.9929402,0.0002221027,0.004958244,0.0001697297,0.00002109918,0.0001020528,0.0002216013,0.00002283159,0.001342138],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003617699,"threshold_uncertainty_score":0.01210243,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1224888743522888,"score_gpt":0.410167969622967,"score_spread":0.2876790952706781,"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."}}