{"id":"W4391432423","doi":"10.1007/978-3-031-52730-2_4","title":"Automated Picoliter-Resolution Sperm Aspiration","year":2023,"lang":"en","type":"book-chapter","venue":"","topic":"Microfluidic and Bio-sensing Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Sperm; Resolution (logic); Computer science; Medicine; Artificial intelligence; Andrology","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002487508,0.0006622931,0.00065767,0.0006525929,0.0004346067,0.0008684497,0.00135348,0.0006581935,0.01154506],"category_scores_gemma":[0.000238154,0.0005054922,0.0003982949,0.0008931725,0.0003077554,0.0008789703,0.001063302,0.000778607,0.01159899],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000506002,"about_ca_system_score_gemma":0.0004894555,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007292236,"about_ca_topic_score_gemma":0.002015335,"domain_scores_codex":[0.9995396,0.00001988277,0.00001473375,0.00006859384,0.0003319869,0.00002519444],"domain_scores_gemma":[0.9999107,0.00002662369,0.000006905333,0.00001722975,0.00003221998,0.000006297254],"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":[0.00007884888,0.00008075714,0.0001974662,0.0006524348,0.00003309314,0.0001457036,0.00009773478,0.00322626,0.2695253,0.01908536,0.03249632,0.6743808],"study_design_scores_gemma":[0.00001737419,0.0001444046,0.002044934,0.0001733616,0.00003793124,0.001233403,0.00005401722,0.02518196,0.3086724,0.009218416,0.6531375,0.00008431567],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01257688,0.02596908,0.7684069,0.0006330623,0.001428751,0.0003886468,0.001251088,0.005927688,0.1834179],"genre_scores_gemma":[0.07238054,0.02909494,0.4109917,0.001049289,0.0004112933,0.0004388306,0.002559487,0.000880859,0.4821932],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01154506,"threshold_uncertainty_score":0.03862208,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02600511355625021,"score_gpt":0.2123309496892581,"score_spread":0.1863258361330078,"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."}}