{"id":"W2047767764","doi":"10.1097/gco.0000000000000072","title":"Time-lapse embryo imaging technology","year":2014,"lang":"en","type":"review","venue":"Current Opinion in Obstetrics & Gynecology","topic":"Reproductive Biology and Fertility","field":"Medicine","cited_by":24,"is_retracted":false,"has_abstract":true,"ca_institutions":"Centre Casa","funders":"","keywords":"Embryo; Scope (computer science); Embryo quality; Selection (genetic algorithm); Medicine; Embryo culture; Risk analysis (engineering); Biochemical engineering; Computer science; Cryopreservation; Biology; Embryogenesis; Artificial intelligence; Cell biology; 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":["metaresearch","metaepi_narrow","research_integrity","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0009865061,0.0006697707,0.003478834,0.002528487,0.00009895854,0.00001214089,0.0005551424,0.001306759,0.0002479413],"category_scores_gemma":[0.02090043,0.0005884996,0.0005262078,0.002380018,0.0007212394,0.00005894075,0.0004120882,0.002257298,0.001384991],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007352434,"about_ca_system_score_gemma":0.0006707141,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001289143,"about_ca_topic_score_gemma":2.824395e-7,"domain_scores_codex":[0.9952952,0.0006162269,0.001496886,0.001559171,0.0002074449,0.0008250637],"domain_scores_gemma":[0.9937615,0.003641757,0.0006967488,0.00144926,0.0002749366,0.0001758016],"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":[0.00003248846,0.0004137185,0.001284536,0.01315491,0.00007132561,0.00001311159,0.00001500786,1.776387e-7,7.159588e-7,0.0002676361,0.003639124,0.9811072],"study_design_scores_gemma":[0.0007973682,0.0001949694,0.001126873,0.004384972,0.0002623106,0.0001934249,0.000009786469,0.00003020915,5.314511e-7,0.0003566158,0.9921985,0.0004443854],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.00004625996,0.9717432,0.00006021348,0.0002568964,0.02538562,0.001724831,0.0000269784,0.0001838068,0.0005722226],"genre_scores_gemma":[0.00005445561,0.9977261,0.0001064818,0.00003323405,0.0007659062,0.0003501817,0.0006053987,0.00006997396,0.0002882893],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.9885594,"threshold_uncertainty_score":0.9999897,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06124755996872124,"score_gpt":0.3845006957655895,"score_spread":0.3232531357968683,"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."}}