{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003845264,0.0009615852,0.0009545829,0.004641375,0.001670124,0.001954432,0.001309336,0.001490868,0.005593465],"category_scores_gemma":[0.002920767,0.0006686096,0.0008830135,0.001336449,0.0009925067,0.001195262,0.001979869,0.001449931,0.005878649],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004439208,"about_ca_system_score_gemma":0.002389772,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001348785,"about_ca_topic_score_gemma":0.002957502,"domain_scores_codex":[0.9974783,0.0005922277,0.0003890703,0.0004864861,0.0008475779,0.0002063024],"domain_scores_gemma":[0.9977624,0.0003067953,0.0002683542,0.0006561922,0.0008223897,0.0001838233],"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":[0.000993117,0.000215509,0.009392936,0.000690606,0.0001061893,0.002834181,0.0007721056,0.000642713,0.8662014,0.003291657,0.006787085,0.1080725],"study_design_scores_gemma":[0.0002083782,0.001556764,0.04092805,0.001296805,0.0003237434,0.01568732,0.001885878,0.007610192,0.7065259,0.004919656,0.2188574,0.0001997964],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1419974,0.01697476,0.8016368,0.00256216,0.001425478,0.007466363,0.01014785,0.00461483,0.01317436],"genre_scores_gemma":[0.2063462,0.01179592,0.7436483,0.003149397,0.0006608419,0.006798327,0.01730015,0.001195812,0.009105044],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005593465,"threshold_uncertainty_score":0.02033591,"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."}}