{"id":"W2899142662","doi":"10.1002/elps.201800345","title":"Automatic detecting and counting magnetic beads‐labeled target cells from a suspension in a microfluidic chip","year":2018,"lang":"en","type":"article","venue":"Electrophoresis","topic":"Microfluidic and Bio-sensing Technologies","field":"Engineering","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"National Natural Science Foundation of China","keywords":"Microfluidics; Microfluidic chip; Materials science; Resistive touchscreen; Magnetic separation; Bead; Chip; Fluorescence; Chromatography; Immunomagnetic separation; Suspension (topology); Magnetic bead; Lab-on-a-chip; Nanotechnology; Analytical Chemistry (journal); Chemistry; Computer science; Optics","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001899467,0.0002578044,0.0003111564,0.000252324,0.000126758,0.00006843625,0.0001660138,0.0002028031,0.00009867164],"category_scores_gemma":[0.00009263198,0.0002515345,0.00003731764,0.0004738272,0.000129968,0.00006028964,0.00008029048,0.0002893473,0.00004785071],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001553327,"about_ca_system_score_gemma":0.00002182823,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003655155,"about_ca_topic_score_gemma":0.0001129796,"domain_scores_codex":[0.9986008,0.00004196128,0.0003193841,0.0003360931,0.0001336239,0.0005681213],"domain_scores_gemma":[0.9994833,0.0001145676,0.00004432768,0.000273609,0.00003534362,0.00004885368],"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":[0.00001271269,0.00001050547,0.0007482726,0.00002691247,0.0000136818,0.00002065253,0.0001739815,5.042856e-7,0.9786136,0.000005477612,0.002924456,0.01744923],"study_design_scores_gemma":[0.0003758131,0.0001508691,0.002828336,0.0001221957,0.00002360294,0.00001520867,0.0001108185,0.01168984,0.9831486,0.000419498,0.0008180206,0.0002972276],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9756644,0.02269956,0.0004010185,0.00005677737,0.0001409539,0.0001870659,0.000004767885,0.0007191989,0.0001262309],"genre_scores_gemma":[0.9916119,0.002177612,0.005983907,0.00004619516,0.00009932059,0.000006881382,0.00000489516,0.00005194506,0.00001734363],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02052194,"threshold_uncertainty_score":0.9999937,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004561494111278971,"score_gpt":0.178255712661976,"score_spread":0.173694218550697,"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."}}