{"id":"W4391045485","doi":"10.15353/cjo.v49i2.4549","title":"Detection of Visual Field Defect Using Topographic Evoked Potential in Children","year":2021,"lang":"en","type":"article","venue":"Canadian journal of optometry/CJO. Canadian journal of optometry","topic":"Neural dynamics and brain function","field":"Neuroscience","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Latency (audio); Visual field; Computer science; Population; Code (set theory); Set (abstract data type); Neuroscience; Cognitive psychology; Psychology; Artificial intelligence; Medicine","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003558222,0.0004466307,0.0004257928,0.001632574,0.0002640684,0.0004774354,0.000323373,0.000593713,0.001539525],"category_scores_gemma":[0.002462491,0.000188395,0.0002220147,0.0005085311,0.0006805212,0.0008085383,0.0004692365,0.0004958207,0.0003428487],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003104302,"about_ca_system_score_gemma":0.0004625857,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005196983,"about_ca_topic_score_gemma":0.002845435,"domain_scores_codex":[0.9996529,0.00004762697,0.00005189746,0.00009577557,0.00008931146,0.00006262537],"domain_scores_gemma":[0.9992894,0.0002125663,0.0002133037,0.00003578099,0.0001438146,0.0001051351],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002547552,0.0000768,0.9511614,0.0001102182,0.0000209208,0.02474456,0.0006742379,0.0002240168,0.009958207,0.0002031775,0.000589156,0.01198261],"study_design_scores_gemma":[0.000009889084,0.0003064901,0.9203303,0.00005035558,0.0000231859,0.07309792,0.001006062,0.0005952145,0.00373242,0.0002426866,0.0005834213,0.00002205475],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9969079,0.0002845296,0.0006513239,0.0001238246,0.00001015093,0.00002444747,0.0003500311,0.00004235666,0.001605537],"genre_scores_gemma":[0.9987293,0.000264264,0.0005347237,0.00004424237,0.000006870377,0.00001178767,0.0001277733,0.000008300844,0.0002727525],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005196983,"threshold_uncertainty_score":0.01033348,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01124605503000997,"score_gpt":0.2822156513288052,"score_spread":0.2709695962987952,"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."}}