{"id":"W2001244621","doi":"10.1149/1.3571976","title":"Microfluidics for Detection of Myoglobin in Blood Samples","year":2011,"lang":"en","type":"article","venue":"ECS Transactions","topic":"Microfluidic and Capillary Electrophoresis Applications","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Alberta Innovates - Technology Futures; CMC Microsystems","keywords":"Myoglobin; Dielectrophoresis; Microfluidics; Immunoassay; Nanotechnology; Materials science; Chromatography; Chemistry; Biology; Biochemistry; Antibody; Immunology","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":[0.00005698898,0.00008503113,0.0001130368,0.0001093082,0.00004586123,0.000003214149,0.00007535489,0.00007175036,0.00008715096],"category_scores_gemma":[0.000002283287,0.00009757606,0.00007127033,0.0002263791,0.00002571132,0.00004728939,9.041018e-7,0.00007816585,0.000004542136],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002441979,"about_ca_system_score_gemma":0.00001428595,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007494316,"about_ca_topic_score_gemma":0.00002802861,"domain_scores_codex":[0.9994874,0.000007518956,0.0002088164,0.0001033681,0.00004067327,0.0001522457],"domain_scores_gemma":[0.999759,0.0000250712,0.00001649568,0.000141847,0.00002694822,0.00003067837],"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":[0.000008970075,0.00007064237,0.0000386862,0.00003462133,0.00003460049,1.573352e-7,0.0002973802,0.00003764438,0.9969744,0.0002411935,0.0002447423,0.002016941],"study_design_scores_gemma":[0.0002807622,0.00004301971,0.0009233697,0.000007200256,0.00005065657,0.000005158595,0.00007702244,0.0001266361,0.9885846,0.0004162036,0.009393472,0.00009190399],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5115035,0.005777985,0.4818858,0.000007558792,0.00007497394,0.0002389398,0.00005619864,0.00008487436,0.0003702255],"genre_scores_gemma":[0.9872482,0.0117121,0.0008023627,0.000005197596,0.00001441704,0.0001562235,0.00000626996,0.00002212414,0.000033047],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4810835,"threshold_uncertainty_score":0.3979036,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01649117522515015,"score_gpt":0.1955776629331982,"score_spread":0.1790864877080481,"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."}}