{"id":"W2792006229","doi":"10.1089/bio.2017.0109","title":"Standard PREanalytical Code Version 3.0","year":2018,"lang":"en","type":"article","venue":"Biopreservation and Biobanking","topic":"Reproductive Health and Technologies","field":"Medicine","cited_by":52,"is_retracted":false,"has_abstract":false,"ca_institutions":"Lunenfeld-Tanenbaum Research Institute","funders":"","keywords":"Computer science; Code (set theory); Programming language","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":[0.0001883168,0.00006105026,0.0001074871,0.00008210527,0.0001207899,0.00001756698,0.00003691496,0.00007605425,0.0001067285],"category_scores_gemma":[0.0002450598,0.00004571368,0.00001864126,0.00015423,0.0001663638,0.00007860452,0.00005268461,0.0000748457,0.00002864989],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003026008,"about_ca_system_score_gemma":0.0000337127,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001859007,"about_ca_topic_score_gemma":0.000006505824,"domain_scores_codex":[0.9993708,0.000009474904,0.0001185804,0.000207164,0.0001585047,0.0001355236],"domain_scores_gemma":[0.9995425,0.00001434276,0.0000348462,0.0001863495,0.0001669799,0.00005494425],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001576306,0.00009049688,0.8378649,0.000236237,0.00005538873,0.00001389155,0.000211496,1.088519e-7,0.04318381,0.01929241,0.02035896,0.07711598],"study_design_scores_gemma":[0.001391181,0.001244477,0.4760954,0.0001393867,0.00005044063,0.00003645093,0.0001919141,0.0008302574,0.07508668,0.00379265,0.4409858,0.0001553583],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9845998,0.0001788806,0.0005787238,0.008658899,0.00009587783,0.0001855564,0.000006094033,0.000157541,0.005538625],"genre_scores_gemma":[0.9965144,0.00008944391,0.002311257,0.0004789476,0.0002087505,0.000002895666,0.00001257102,0.000005106796,0.0003766264],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4206268,"threshold_uncertainty_score":0.186415,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04715415545029655,"score_gpt":0.3405276771910874,"score_spread":0.2933735217407908,"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."}}