{"id":"W2124057135","doi":"10.1089/bio.2011.0020","title":"A Proposed Schema for Classifying Human Research Biobanks","year":2011,"lang":"en","type":"article","venue":"Biopreservation and Biobanking","topic":"Biomedical Text Mining and Ontologies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":67,"is_retracted":false,"has_abstract":true,"ca_institutions":"BC Cancer Agency","funders":"BC Cancer Agency; Michael Smith Health Research BC","keywords":"Biobank; Schema (genetic algorithms); Computer science; Data science; Computational biology; Information retrieval; Bioinformatics; Biology","routes":{"ca_aff":true,"ca_fund":true,"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":[0.000767156,0.0001149523,0.0001117689,0.000100993,0.0003662942,0.00004585513,0.0001965097,0.0002546819,0.00002219412],"category_scores_gemma":[0.0002634442,0.00009540979,0.00004936359,0.000142028,0.0002916813,0.000006337539,0.0001637832,0.0001138695,0.000003365619],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001044525,"about_ca_system_score_gemma":0.00003850865,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006230689,"about_ca_topic_score_gemma":0.00003959431,"domain_scores_codex":[0.9988921,0.00005952307,0.0001947417,0.0003722181,0.0001650628,0.000316322],"domain_scores_gemma":[0.9994268,0.00002271816,0.00006075071,0.000230815,0.0001862353,0.00007261099],"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":[0.0001460658,0.000089142,0.02339149,0.00009343056,0.00003790175,0.000001594603,0.0003095239,2.593078e-8,0.9273738,0.004201856,0.003675245,0.04067989],"study_design_scores_gemma":[0.001861414,0.001556547,0.05542929,0.0001290547,0.00002419958,0.00001320065,0.001008276,0.000202191,0.6943237,0.00618879,0.2387386,0.0005247032],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.993773,0.0005969002,0.00263795,0.0003778847,0.00009836709,0.0003575177,0.000009740707,0.00005065611,0.002097921],"genre_scores_gemma":[0.9847744,0.00004956449,0.01398128,0.0001534202,0.0002139237,0.00004629005,0.00007345365,0.00001568027,0.0006920067],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2350633,"threshold_uncertainty_score":0.3890698,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.4782123450186061,"score_gpt":0.4320864660738816,"score_spread":0.04612587894472447,"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."}}