{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006295678,0.0002786578,0.0003242723,0.0005284272,0.0001549007,0.0003728744,0.0004892216,0.0004099293,0.001540663],"category_scores_gemma":[0.001289267,0.0001582659,0.0002558161,0.0002737525,0.0002334622,0.0002141731,0.0004245271,0.0003461658,0.000504924],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004619452,"about_ca_system_score_gemma":0.000479903,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002480652,"about_ca_topic_score_gemma":0.004496729,"domain_scores_codex":[0.999844,0.00003047357,0.000006688673,0.00004499306,0.00004848846,0.00002534833],"domain_scores_gemma":[0.9996091,0.0001368456,0.00005900978,0.00002732977,0.0001317332,0.00003619308],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000782333,0.000290068,0.07354627,0.0002786969,0.0001641114,0.0004505142,0.0001578225,0.1827239,0.2994372,0.002135734,0.008176615,0.4318567],"study_design_scores_gemma":[0.00002184934,0.0001801519,0.01434394,0.000029893,0.00003239011,0.0001886984,0.00003094954,0.8999529,0.08223432,0.001033353,0.001932413,0.00001912543],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7052335,0.001545951,0.2852865,0.0007871759,0.0001169563,0.00009035897,0.000885268,0.002536602,0.003517636],"genre_scores_gemma":[0.933036,0.0002142194,0.06122814,0.0003416909,0.0000268834,0.00003935811,0.0007440336,0.00008169436,0.004287864],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002480652,"threshold_uncertainty_score":0.005154014,"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."}}