{"id":"W2919745482","doi":"","title":"Identification of protein components of the rod outer segment plasma membrane by label-free protein correlation profiling","year":2018,"lang":"en","type":"article","venue":"Investigative Ophthalmology & Visual Science","topic":"Machine Learning in Bioinformatics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Profiling (computer programming); Identification (biology); Chemistry; Chromatography; Biology; Computer science; Botany","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":[0.0009140365,0.0001480612,0.0001577268,0.00006083914,0.0002129049,0.00001718735,0.0007671649,0.0001237269,0.00001197025],"category_scores_gemma":[0.001133761,0.0001135762,0.00003767972,0.0003913124,0.003656214,0.00002699538,0.000448349,0.000145169,0.000007800091],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003124799,"about_ca_system_score_gemma":0.0001631835,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004215986,"about_ca_topic_score_gemma":0.000001776014,"domain_scores_codex":[0.9983165,0.0002003404,0.0005004813,0.0003291622,0.0004190902,0.0002344623],"domain_scores_gemma":[0.9983647,0.00001653741,0.0006916028,0.0004728758,0.0003857244,0.00006852933],"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":[0.0000367797,0.00008359833,0.007596317,0.0000422692,0.00001416485,3.244754e-7,0.0002664633,0.00005305066,0.9916767,0.0001342938,0.00002124653,0.0000747915],"study_design_scores_gemma":[0.0003545702,0.0006031109,0.009498828,0.00006081859,0.0000082621,0.00001822285,0.00005200988,0.009859666,0.9790818,0.0003282243,0.00002228565,0.0001121762],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9982436,0.00001344036,0.0001643622,0.0001690523,0.0001435501,0.0008345514,0.0000288557,0.000007029767,0.0003955716],"genre_scores_gemma":[0.9952833,3.495745e-7,0.004155337,0.00003306295,0.00003321306,0.00004896438,0.0000324619,0.00000967605,0.0004036523],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01259488,"threshold_uncertainty_score":0.9990553,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01979260656538069,"score_gpt":0.299699987317554,"score_spread":0.2799073807521733,"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."}}