{"id":"W4255414597","doi":"10.3410/f.727208030.793535613","title":"Faculty Opinions recommendation of Examining the efficacy of six published time-lapse imaging embryo selection algorithms to predict implantation to demonstrate the need for the development of specific, in-house morphokinetic selection algorithms.","year":2017,"lang":"en","type":"dataset","venue":"Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature","topic":"Reproductive Biology and Fertility","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Lunenfeld-Tanenbaum Research Institute; University of Toronto; Mount Sinai Hospital","funders":"","keywords":"Selection (genetic algorithm); Computer science; Embryo; Algorithm; Machine learning; Artificial intelligence; Biology; Genetics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.002586903,0.001853058,0.001084036,0.002558974,0.0006713748,0.002359381,0.00236663,0.002224777,0.04110563],"category_scores_gemma":[0.01404211,0.0004816925,0.002065507,0.002016142,0.0003865879,0.001099481,0.001533483,0.001586728,0.04714656],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001288862,"about_ca_system_score_gemma":0.002848028,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01771002,"about_ca_topic_score_gemma":0.05009637,"domain_scores_codex":[0.9986721,0.0002555167,0.0001315143,0.0004082428,0.0004033074,0.0001292597],"domain_scores_gemma":[0.9940963,0.002248253,0.0004337309,0.001159781,0.001513833,0.0005482033],"domain_codex":null,"domain_gemma":"evaluation","domain_candidate":"evaluation","domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0002051428,0.00007811698,0.004450908,0.0007647887,0.000133624,0.00002520069,0.00001091965,0.0005700972,0.0002112218,0.0002698687,0.9849219,0.008358252],"study_design_scores_gemma":[0.001523996,0.0001473994,0.02202244,0.0009409904,0.0003278599,0.0001973561,0.0001054176,0.006896311,0.002404622,0.002147588,0.9632,0.00008601866],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.001339461,0.0003099226,0.0004295232,0.0005612338,0.0001901802,0.00006703346,0.9937261,0.001035572,0.002340998],"genre_scores_gemma":[0.002203881,0.0001472168,0.001179076,0.0002574478,0.00004660966,0.000125897,0.9941964,0.0001148464,0.001728478],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.9974131,"threshold_uncertainty_score":0.137512,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04119661129051085,"score_gpt":0.3588123085248978,"score_spread":0.317615697234387,"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."}}