{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007845347,0.0005507174,0.0006607837,0.0009112023,0.000320719,0.000559004,0.001148165,0.0005898061,0.0004035021],"category_scores_gemma":[0.0007367634,0.000486525,0.0002685876,0.0003028304,0.0003805513,0.0004804939,0.0005405487,0.0007062673,0.0003638597],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003482728,"about_ca_system_score_gemma":0.0005839724,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004484733,"about_ca_topic_score_gemma":0.0007330481,"domain_scores_codex":[0.999243,0.00009155699,0.00006418825,0.0002083336,0.0003348141,0.00005806788],"domain_scores_gemma":[0.9995435,0.000202218,0.00005656524,0.00004264701,0.000114902,0.00004015447],"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":[0.00004091882,0.00002695061,0.0002618731,0.00006658606,0.000006721056,0.00002216611,0.00001942698,0.0001614637,0.985678,0.0002234374,0.0001551218,0.01333742],"study_design_scores_gemma":[0.00001923399,0.0001143353,0.001136238,0.000008527528,0.00002443962,0.0002193713,0.00001004785,0.01116569,0.9841376,0.0001072136,0.003029295,0.00002799511],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1818567,0.002657006,0.8098323,0.0003002706,0.0003654203,0.0004717,0.0004063383,0.00249908,0.001611147],"genre_scores_gemma":[0.2873418,0.001573082,0.70717,0.0002288767,0.0001380634,0.000713168,0.000343245,0.00006048387,0.00243125],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001148165,"threshold_uncertainty_score":0.00414902,"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."}}