{"id":"W3163193946","doi":"10.1039/d0lc01182g","title":"Selection of high-quality sperm with thousands of parallel channels","year":2021,"lang":"en","type":"article","venue":"Lab on a Chip","topic":"Sperm and Testicular Function","field":"Medicine","cited_by":27,"is_retracted":false,"has_abstract":true,"ca_institutions":"Mount Sinai Hospital; Ottawa Fertility Centre; University of Toronto; Toronto Public Health","funders":"Australian Research Council; Canadian Institutes of Health Research; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Sperm; Selection (genetic algorithm); Sperm quality; Yield (engineering); Quality (philosophy); Biology; Computer science; Computational biology; Artificial intelligence; Genetics; Materials science; Physics","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":[0.0001092063,0.00007360475,0.0002306339,0.0000408613,0.0000173694,0.000002942511,0.00001824201,0.00005242043,0.0003271791],"category_scores_gemma":[0.0001087406,0.00005403263,0.00004614707,0.0002449272,0.00002859259,0.00002069837,0.000007208272,0.00008621432,0.000005905239],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001837632,"about_ca_system_score_gemma":0.00006835349,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009686322,"about_ca_topic_score_gemma":0.00003225104,"domain_scores_codex":[0.9993579,0.00004138314,0.0001577674,0.0001456792,0.0002072617,0.00009003644],"domain_scores_gemma":[0.9995146,0.00004435338,0.00008162538,0.0001657835,0.0001510318,0.00004257221],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.005065735,0.003036387,0.4471001,0.001259688,0.0006418818,0.000146082,0.001232789,0.0006066306,0.4804309,0.0487442,0.001034534,0.01070105],"study_design_scores_gemma":[0.002626983,0.001146967,0.5578169,0.0002049575,0.0001164248,0.00009051972,0.00008573728,0.0001109659,0.4369621,0.000389596,0.00035836,0.00009052332],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9947385,0.00004253668,0.000642837,0.0002948236,0.0001123219,0.0001008936,0.000003932803,0.00002973752,0.0040344],"genre_scores_gemma":[0.9975385,0.00001527934,0.001212519,0.0001852766,0.0001227482,0.000004612455,0.00002948855,0.00001070147,0.0008808776],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1107167,"threshold_uncertainty_score":0.3582382,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02385245824315024,"score_gpt":0.2774784387035736,"score_spread":0.2536259804604234,"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."}}