{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008561214,0.0005247811,0.0005976218,0.0009562183,0.0003009516,0.0006796832,0.0008645609,0.0006764051,0.0008214547],"category_scores_gemma":[0.002042738,0.0003881884,0.0003809636,0.0007044305,0.0004538508,0.0008957155,0.0007896831,0.0005005844,0.0004445071],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000308019,"about_ca_system_score_gemma":0.0005908281,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008431677,"about_ca_topic_score_gemma":0.001569619,"domain_scores_codex":[0.9989551,0.0001404508,0.00006750306,0.0002404355,0.0005499767,0.00004651822],"domain_scores_gemma":[0.99849,0.0004630017,0.0003072622,0.00030109,0.0003928035,0.00004591054],"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":[0.0001275045,0.00006526101,0.01116119,0.0005292233,0.0000632643,0.0001856836,0.000187281,0.006213987,0.6897376,0.002206788,0.001024759,0.2884974],"study_design_scores_gemma":[0.00003459338,0.0008522937,0.06219811,0.00007732343,0.0001334453,0.002012706,0.0001650046,0.1862961,0.7287512,0.003462104,0.01582308,0.0001941598],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.07969724,0.001831949,0.9148962,0.0001594942,0.00007922857,0.00009467969,0.000276252,0.001330934,0.001633938],"genre_scores_gemma":[0.4879668,0.002021421,0.5063263,0.0001359608,0.00008507798,0.0002134033,0.0005082514,0.0001827402,0.002560037],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.0009562183,"threshold_uncertainty_score":0.004527628,"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."}}