{"id":"W2745017788","doi":"10.1039/c7lc00653e","title":"Microparticle parking and isolation for highly sensitive microRNA detection","year":2017,"lang":"en","type":"article","venue":"Lab on a Chip","topic":"Innovative Microfluidic and Catalytic Techniques Innovation","field":"Engineering","cited_by":22,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Cancer Institute; Natural Sciences and Engineering Research Council of Canada; Samsung; National Institutes of Health","keywords":"Microfluidics; Microparticle; Dispersity; Nanotechnology; Particle (ecology); Chemistry; Materials science; Biological system; Chromatography; Chemical engineering","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004577595,0.0003836722,0.000311179,0.0003321094,0.0002560787,0.0002970455,0.0004369339,0.0003610197,0.0006630493],"category_scores_gemma":[0.0005234944,0.000214997,0.000256419,0.0001381238,0.0003764237,0.0003600229,0.0003668131,0.0004487924,0.0004262375],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003590663,"about_ca_system_score_gemma":0.0002916847,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003373681,"about_ca_topic_score_gemma":0.0006472669,"domain_scores_codex":[0.9996693,0.00004852735,0.00003306433,0.00009490961,0.0001201573,0.00003412139],"domain_scores_gemma":[0.9997173,0.0001288409,0.00006228714,0.00002488046,0.00004070106,0.00002587174],"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":[0.00001417892,0.000008030182,0.0000733062,0.00003400623,0.000002267403,0.00002912571,0.00001307371,0.00005922104,0.9971681,0.0001562356,0.00004303335,0.002399383],"study_design_scores_gemma":[0.000003807806,0.00003520263,0.0002125758,0.000001883581,0.000003707847,0.00008841084,0.000003911922,0.001123816,0.9976769,0.00004094761,0.0008040431,0.000004815382],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6737908,0.00357487,0.3184187,0.0004186689,0.0001672657,0.0002926191,0.0002904735,0.001034405,0.002012345],"genre_scores_gemma":[0.814298,0.001321067,0.1808497,0.0002348657,0.00005418788,0.0002242738,0.0002878724,0.0001145992,0.002615406],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0006630493,"threshold_uncertainty_score":0.002605259,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01543744195671405,"score_gpt":0.2443111802034092,"score_spread":0.2288737382466951,"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."}}