{"id":"W2169437479","doi":"10.1109/coase.2010.5584613","title":"Detection and tracking of low contrast human sperm tail","year":2010,"lang":"en","type":"article","venue":"","topic":"Sperm and Testicular Function","field":"Medicine","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Sperm; Tracking (education); Sperm motility; Computer science; Algorithm; Computer vision; Intracytoplasmic sperm injection; Artificial intelligence; Biology; Biological system; In vitro fertilisation; Cell biology; Genetics; Embryo","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00006411521,0.00004215512,0.00008604823,0.00003681446,0.00003121431,0.000005657105,0.000008994666,0.00004866809,0.0003804062],"category_scores_gemma":[0.0000753518,0.00003279551,0.00002170724,0.00003882544,0.00004075056,0.0000356364,0.00000406167,0.00009580064,0.000005096326],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000004153962,"about_ca_system_score_gemma":0.000006588718,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000683558,"about_ca_topic_score_gemma":0.0002216658,"domain_scores_codex":[0.9997069,0.000003556233,0.00008520293,0.00007918903,0.00006669015,0.00005851896],"domain_scores_gemma":[0.9997888,0.00001667963,0.00002357286,0.00007760847,0.00005191794,0.00004148082],"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":[0.00001540457,0.00003634481,0.02896292,0.0000261108,0.000008585776,0.000003128458,0.0000433488,8.197794e-8,0.9527298,0.0003569379,0.00001247393,0.01780493],"study_design_scores_gemma":[0.0009339042,0.0001821846,0.4996319,0.00002118568,0.00004595314,0.0001203427,0.00008621937,0.0002997847,0.4982984,0.00009281147,0.000243021,0.00004432139],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9925158,0.00001425506,0.0007944932,0.0000496838,0.0001470108,0.00008929719,2.990602e-7,0.0000337562,0.006355396],"genre_scores_gemma":[0.9993554,0.0000017245,0.0001665532,0.00005292114,0.0001311832,0.00000142369,0.000002011766,0.00000567826,0.0002830565],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.470669,"threshold_uncertainty_score":0.4165182,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01183755725706964,"score_gpt":0.2472045326048864,"score_spread":0.2353669753478167,"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."}}