{"id":"W4386618747","doi":"10.3791/65804","title":"Biobanking of Human Aqueous and Vitreous Liquid Biopsies for Molecular Analyses","year":2023,"lang":"en","type":"article","venue":"Journal of Visualized Experiments","topic":"Retinal Imaging and Analysis","field":"Medicine","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"National Institutes of Health; H. Lundbeck A/S; Lundbeckfonden; National Eye Institute; VitreoRetinal Surgery Foundation; Research to Prevent Blindness","keywords":"Workflow; Biobank; Computer science; Barcode; Interface (matter); Identification (biology); Medical physics; Computational biology; Bioinformatics; Database; Medicine; Biology; Operating system","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":[],"consensus_categories":[],"category_scores_codex":[0.0003107109,0.0001281241,0.0005766223,0.0005389045,0.00006714057,0.00002376149,0.00007870282,0.00004432086,0.00001889523],"category_scores_gemma":[0.0001448129,0.00009868532,0.0003071737,0.0003491856,0.00008121858,0.0000630674,0.00003469555,0.00007788069,0.000001398433],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002677779,"about_ca_system_score_gemma":0.00003398065,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002683385,"about_ca_topic_score_gemma":1.515411e-7,"domain_scores_codex":[0.9987653,0.00004811102,0.0005582937,0.000126844,0.0003268049,0.0001745857],"domain_scores_gemma":[0.9990434,0.0000570216,0.000424304,0.0001230932,0.000244207,0.0001079987],"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":[0.0005115247,0.0001287317,0.0008104293,0.00008450175,0.0005635883,0.0001617741,0.0005844623,0.000008864497,0.996241,0.00002690775,0.0002737385,0.000604516],"study_design_scores_gemma":[0.002123181,0.001508408,0.0002968798,0.0003302039,0.0005191539,0.0001393293,0.001068441,0.0004726348,0.9930503,0.00009837787,0.0002966087,0.00009650545],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9958625,0.003037752,0.0006201415,0.0001994515,0.00005552476,0.0000940792,0.000002908754,0.00001992294,0.0001077199],"genre_scores_gemma":[0.9974643,0.0002135431,0.001959107,0.00009773238,0.00007516157,0.000003902682,0.000007812563,0.00002193957,0.0001565143],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003190684,"threshold_uncertainty_score":0.4024271,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08636430653120891,"score_gpt":0.5372635648557612,"score_spread":0.4508992583245523,"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."}}