{"id":"W2804200704","doi":"10.1109/tmi.2018.2840827","title":"Automated Non-Invasive Measurement of Single Sperm’s Motility and Morphology","year":2018,"lang":"en","type":"article","venue":"IEEE Transactions on Medical Imaging","topic":"Sperm and Testicular Function","field":"Medicine","cited_by":50,"is_retracted":false,"has_abstract":true,"ca_institutions":"Mount Sinai Hospital; CReATe Fertility Centre; University of Toronto","funders":"University of Toronto; Science and Technology Commission of Shanghai Municipality; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Morphology (biology); Motility; Sperm motility; Computer science; Computer vision; Artificial intelligence; Biology; Cell biology; Zoology","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":[0.0003477469,0.0001206545,0.000236577,0.0001276064,0.00008525719,0.000006057488,0.00004485258,0.00008234373,0.0006744451],"category_scores_gemma":[0.0002079949,0.0001025355,0.00006622606,0.0001676921,0.0004869282,0.00003961806,0.000001417707,0.0002016604,0.00003329564],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008493118,"about_ca_system_score_gemma":0.0001487618,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001109097,"about_ca_topic_score_gemma":0.00003480009,"domain_scores_codex":[0.9986675,0.00004557187,0.0002576025,0.0002544182,0.0005828989,0.0001919751],"domain_scores_gemma":[0.9991645,0.00008410919,0.00004772702,0.0002041434,0.000270148,0.0002293494],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0003413841,0.001822705,0.008721407,0.0002702926,0.0002505248,0.0002608916,0.0007232193,0.0000298806,0.8798036,0.000005821043,0.00122098,0.1065493],"study_design_scores_gemma":[0.004429139,0.001079677,0.05700381,0.0008212765,0.0005110701,0.001193817,0.0003458807,0.0820675,0.8520935,0.00005321101,0.0001733865,0.0002277618],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7459476,0.00005653208,0.2510384,0.001219832,0.0006210438,0.0001915899,0.000004047403,0.0001818914,0.0007390277],"genre_scores_gemma":[0.9985247,0.00001577632,0.0007147,0.0005908544,0.0001042321,0.000008280573,0.000001677539,0.00001453704,0.00002519747],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2525772,"threshold_uncertainty_score":0.7384701,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02454025179811279,"score_gpt":0.2742644461445292,"score_spread":0.2497241943464164,"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."}}