{"id":"W4399033088","doi":"10.1016/j.rbmo.2024.103996","title":"Using simulation to optimize IVF lab resources and meet physiological time constraints","year":2024,"lang":"en","type":"article","venue":"Reproductive BioMedicine Online","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"St. Thomas Hospital","funders":"","keywords":"Computer science; Biochemical engineering; Environmental science; Engineering","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":[0.001492737,0.0001795282,0.0003018165,0.0003175059,0.0002663877,0.00006974988,0.0001529175,0.00008657924,0.0001605917],"category_scores_gemma":[0.0006224836,0.0001445304,0.00006072662,0.001083235,0.001254819,0.0001597396,0.0001129724,0.0001240576,0.00002428542],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001017305,"about_ca_system_score_gemma":0.00005458166,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003794775,"about_ca_topic_score_gemma":0.00003144016,"domain_scores_codex":[0.997788,0.0002439499,0.0003016054,0.0008452989,0.0004919073,0.0003292809],"domain_scores_gemma":[0.9991657,0.0001320356,0.00007184764,0.0003047942,0.0001748808,0.0001507902],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001538145,0.0038576,0.02328246,0.001441008,0.003266263,0.00101974,0.1364088,0.05524904,0.2237075,0.03665309,0.04614135,0.4674349],"study_design_scores_gemma":[0.002557564,0.001650423,0.2128911,0.001688729,0.000900481,0.0000314205,0.02374738,0.09309387,0.0004741225,0.01152816,0.6490948,0.002342044],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9858533,0.001426768,0.001069288,0.00716964,0.0006634682,0.0008227041,0.00008939323,0.0002851052,0.002620386],"genre_scores_gemma":[0.9899709,0.00013388,0.006201233,0.0002738842,0.002602851,0.000007640142,0.00003296043,0.00001869073,0.0007580161],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6029534,"threshold_uncertainty_score":0.5893779,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06783351824730675,"score_gpt":0.3936361021304468,"score_spread":0.32580258388314,"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."}}