{"id":"W2992698293","doi":"","title":"International Multicenter Normative ERG Database Using the ISCEV Standard","year":2006,"lang":"en","type":"article","venue":"Investigative Ophthalmology & Visual Science","topic":"Advanced Research in Systems and Signal Processing","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Normative; Erg; Medicine; Computer science; Ophthalmology; Political science","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":["sts"],"consensus_categories":[],"category_scores_codex":[0.0007802739,0.0001731955,0.0001568373,0.0001366645,0.000536204,0.0001386493,0.0006041704,0.00004606054,0.00008986157],"category_scores_gemma":[0.0002338526,0.0001205279,0.00003358472,0.00058753,0.002955901,0.0009879037,0.0002053368,0.0003223444,0.00002078288],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002559009,"about_ca_system_score_gemma":0.0001320889,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001009,"about_ca_topic_score_gemma":0.000002486229,"domain_scores_codex":[0.9982395,0.00009360418,0.0002873355,0.0003026346,0.0006028602,0.0004740992],"domain_scores_gemma":[0.9991993,0.0001400952,0.00008323727,0.0001672127,0.0002810166,0.0001291283],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00001427609,0.00002117895,0.003498295,0.0000204096,0.00001560861,0.00008559965,0.0005084213,0.02229765,0.9729562,0.0002363635,0.0001330293,0.000212928],"study_design_scores_gemma":[0.0004887399,0.0001074427,0.003746784,0.0001760557,0.000009703908,0.0002730689,0.001063093,0.3638718,0.6245195,0.003887131,0.001466253,0.0003905],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.985356,0.0001119302,0.004292348,0.000115013,0.0006016823,0.0001993868,0.00004061702,0.00006285809,0.009220152],"genre_scores_gemma":[0.9940169,0.000002740208,0.00562252,0.00004256568,0.0001993424,0.00001778039,0.000007017668,0.00001562936,0.00007554142],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3484367,"threshold_uncertainty_score":0.9997575,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04524606715216092,"score_gpt":0.360734109162704,"score_spread":0.3154880420105431,"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."}}