{"id":"W2954602225","doi":"10.1038/s42003-019-0491-6","title":"Deep learning-based selection of human sperm with high DNA integrity","year":2019,"lang":"en","type":"article","venue":"Communications Biology","topic":"Sperm and Testicular Function","field":"Medicine","cited_by":131,"is_retracted":false,"has_abstract":true,"ca_institutions":"Mount Sinai Hospital; Ottawa Fertility Centre; Canada Research Chairs; University of Toronto; University of New Brunswick","funders":"Canadian Institutes of Health Research; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; Government of Canada; California HIV/AIDS Research Program","keywords":"Sperm; Biology; DNA; Sperm motility; Selection (genetic algorithm); Sperm quality; Convolutional neural network; Computational biology; Artificial intelligence; Andrology; Computer science; Genetics; Medicine","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0001203195,0.00006829816,0.0001692826,0.0000951308,0.00008653969,0.000002638868,0.0001389323,0.0000994541,0.000359299],"category_scores_gemma":[0.00009363785,0.00005256649,0.0000350578,0.0001990843,0.0001713434,0.00001932478,0.00004036747,0.0003256364,0.00005302355],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004318585,"about_ca_system_score_gemma":0.00005228612,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002230256,"about_ca_topic_score_gemma":0.000213593,"domain_scores_codex":[0.9994865,0.0001294199,0.000140239,0.0001154866,0.00004122859,0.00008710444],"domain_scores_gemma":[0.9989086,0.0001246782,0.00009071836,0.0006609986,0.0001861934,0.00002876197],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0000759402,0.0002545299,0.8933648,0.00002638491,0.00005086855,2.091682e-7,0.00005725191,0.00005421846,0.09556025,0.008966771,0.0000193751,0.001569352],"study_design_scores_gemma":[0.003230555,0.004959696,0.9354854,0.0001310311,0.000235218,0.00006174193,0.0002536747,0.01557679,0.03190674,0.0006742788,0.007257915,0.0002269829],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9936801,0.0001348214,0.001837878,0.0006458398,0.00004022859,0.0002184152,9.32228e-7,0.00006857552,0.003373249],"genre_scores_gemma":[0.996383,0.00002231289,0.002963566,0.0000897361,0.00001926345,0.00001552915,0.0003225758,0.000009092496,0.0001748884],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.06365351,"threshold_uncertainty_score":0.3934073,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02768716741292089,"score_gpt":0.3005124041002011,"score_spread":0.2728252366872803,"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."}}