{"id":"W2911874031","doi":"10.1039/c8lc01204k","title":"Live sperm trap microarray for high throughput imaging and analysis","year":2019,"lang":"en","type":"article","venue":"Lab on a Chip","topic":"Reproductive Biology and Fertility","field":"Medicine","cited_by":24,"is_retracted":false,"has_abstract":true,"ca_institutions":"Mount Sinai Hospital; Ottawa Fertility Centre; University of Toronto; Toronto Public Health","funders":"Natural Sciences and Engineering Research Council of Canada; National Research Foundation of Korea; Ministry of Education; Canadian Institutes of Health Research; National Research Foundation","keywords":"Throughput; Trap (plumbing); Sperm; Microarray; Computational biology; Biology; Computer science; Chemistry; Engineering; Genetics; Telecommunications; Gene; Gene expression; Wireless","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":[0.0001974966,0.0000950812,0.0002776898,0.00006163073,0.00003925478,0.000006352258,0.00003755428,0.00005220004,0.0001907717],"category_scores_gemma":[0.00006761733,0.0000689002,0.00009461063,0.0001191467,0.00007498833,0.00003087959,0.00001260995,0.0001001266,0.00004962656],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000191699,"about_ca_system_score_gemma":0.00001505367,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003641334,"about_ca_topic_score_gemma":0.000006561486,"domain_scores_codex":[0.9992603,0.00003000476,0.0001117407,0.0004044015,0.00004982695,0.0001437335],"domain_scores_gemma":[0.9994742,0.0000682535,0.00003754672,0.0003297932,0.00004684411,0.00004330996],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.002634704,0.0001487927,0.9492632,0.00007090734,0.0005666742,0.000004368421,0.0008703241,8.7063e-7,0.04060161,0.0007182367,0.000325605,0.004794664],"study_design_scores_gemma":[0.0009315693,0.0001992727,0.9867116,0.00001355537,0.0003235511,0.000005981397,0.0001092236,0.00006415247,0.009185573,0.0006835126,0.001692953,0.00007906622],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9950551,0.0005008778,0.0001440915,0.00194367,0.0001356961,0.0003850837,0.00002040679,0.00002658082,0.001788489],"genre_scores_gemma":[0.9965308,0.00002088817,0.0006995006,0.001004655,0.0001362484,0.00001005885,0.00004630227,0.000007552323,0.001544042],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03744835,"threshold_uncertainty_score":0.2809669,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01522817795474422,"score_gpt":0.2763958901194509,"score_spread":0.2611677121647067,"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."}}