{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008111781,0.0009024479,0.0008595126,0.001039335,0.0006334649,0.0009104464,0.001191198,0.001188239,0.02066411],"category_scores_gemma":[0.0005493551,0.000662596,0.0006089209,0.000813527,0.0002694308,0.0006393233,0.0005937215,0.001408121,0.01097219],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006062282,"about_ca_system_score_gemma":0.0005984483,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005855868,"about_ca_topic_score_gemma":0.002017267,"domain_scores_codex":[0.9991001,0.0001156465,0.00004584143,0.0002294508,0.0004318456,0.00007717272],"domain_scores_gemma":[0.9995974,0.0001198251,0.00005586666,0.00007793799,0.0001118046,0.00003724586],"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":[0.0001128209,0.00008345729,0.0003638825,0.0003464799,0.00005151132,0.0001053963,0.00005405357,0.0007075634,0.9467646,0.001974123,0.01672225,0.03271386],"study_design_scores_gemma":[0.00005794238,0.000372177,0.003895175,0.00005868098,0.00005667932,0.0005593572,0.00004420489,0.02861207,0.8518482,0.001492965,0.1128859,0.0001165479],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04694856,0.003472633,0.868001,0.002178903,0.0008240642,0.0007957353,0.02233438,0.03145027,0.02399439],"genre_scores_gemma":[0.09180541,0.002780153,0.843174,0.001785534,0.0002707072,0.003987284,0.01706312,0.001726883,0.03740696],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02066411,"threshold_uncertainty_score":0.06912839,"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."}}