{"id":"W2596335093","doi":"10.1089/bio.2017.29017.bjs","title":"ISBER Goes Global","year":2017,"lang":"en","type":"article","venue":"Biopreservation and Biobanking","topic":"Reproductive Health and Technologies","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Winnipeg","funders":"","keywords":"Biopreservation; Biobank; Library science; Biology; Computer science; Bioinformatics","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":[0.000118545,0.00006018293,0.00009603406,0.00002312726,0.0003110441,0.00006837974,0.00007998877,0.00007084822,0.00002473783],"category_scores_gemma":[0.0003472063,0.00004431401,0.00001965927,0.0000358669,0.0001163525,0.0001522921,0.00009106755,0.00005882304,0.00001284038],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002095702,"about_ca_system_score_gemma":0.00002236195,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001730525,"about_ca_topic_score_gemma":0.00001706101,"domain_scores_codex":[0.9994947,0.000004817874,0.00009430885,0.0001837332,0.00009812973,0.0001243482],"domain_scores_gemma":[0.9994216,0.000004902609,0.00007703045,0.0003869823,0.00006715847,0.00004233667],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00004100676,0.00001741941,0.9282109,0.00004968165,0.000008779507,0.000004980909,0.00001321631,1.882725e-8,0.002198329,0.0109011,0.001064204,0.05749044],"study_design_scores_gemma":[0.0003312426,0.00005252485,0.9482254,0.00003757216,0.000009060831,0.00001677812,0.00003187593,0.00003919269,0.002587248,0.004032782,0.04458546,0.0000508565],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9673523,0.0004628749,0.00005960315,0.01863007,0.0001325547,0.0001581255,0.000003156261,0.0001064023,0.01309492],"genre_scores_gemma":[0.9976132,0.0001740435,0.001187482,0.0004166499,0.0001426499,0.000005822353,0.000005026096,0.000003034586,0.0004521009],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05743958,"threshold_uncertainty_score":0.2392331,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07292245840210554,"score_gpt":0.3893229902006714,"score_spread":0.3164005317985659,"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."}}