{"id":"W958499519","doi":"","title":"Mapping Perimetry Data Onto the TSNIT Curve of Nerve Fiber Layer Thickness","year":2009,"lang":"en","type":"article","venue":"Investigative Ophthalmology & Visual Science","topic":"Infrared Thermography in Medicine","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université Laval","funders":"","keywords":"Nerve fiber layer; Layer (electronics); Ophthalmology; Materials science; Nerve fiber; Fiber; Medicine; Anatomy; Composite material; Optic nerve","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":[0.0005105895,0.0003661875,0.0002296129,0.001591203,0.0001829528,0.0006588751,0.0001934036,0.000322375,0.006133105],"category_scores_gemma":[0.003788951,0.00008798461,0.000219472,0.001040257,0.0002451473,0.0005633005,0.0003341977,0.000415962,0.001008401],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001949679,"about_ca_system_score_gemma":0.0003326696,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003080748,"about_ca_topic_score_gemma":0.002687352,"domain_scores_codex":[0.9997444,0.00005774851,0.00002810382,0.00005917292,0.0000790243,0.0000316227],"domain_scores_gemma":[0.9984626,0.0007328977,0.0001649549,0.0001314854,0.000425892,0.00008224665],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.009321705,0.0003524874,0.2222609,0.0007435903,0.0001809591,0.0009064479,0.002048739,0.009928977,0.2313176,0.003354011,0.005190048,0.5143946],"study_design_scores_gemma":[0.0001267983,0.00193296,0.7626015,0.0002196086,0.0001644174,0.00553754,0.001667276,0.08937222,0.1251402,0.003665386,0.009420697,0.0001513848],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9237587,0.0004546006,0.05299098,0.0001861884,0.0001255194,0.0002186919,0.002349154,0.001812374,0.0181037],"genre_scores_gemma":[0.9757345,0.000276604,0.01978668,0.00006866713,0.0000211605,0.000113457,0.0005652252,0.0001718607,0.003261905],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.006133105,"threshold_uncertainty_score":0.02051729,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08986010021677718,"score_gpt":0.3837301847997913,"score_spread":0.2938700845830141,"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."}}