{"id":"W3016970636","doi":"10.1002/smll.201907534","title":"A Counter Propagating Lens‐Mirror System for Ultrahigh Throughput Single Droplet Detection","year":2020,"lang":"en","type":"article","venue":"Small","topic":"Innovative Microfluidic and Catalytic Techniques Innovation","field":"Engineering","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Eidgenössische Technische Hochschule Zürich; Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung","keywords":"Microfluidics; Microscale chemistry; Throughput; Detector; Materials science; Lens (geology); Optoelectronics; Nanotechnology; Realization (probability); Channel (broadcasting); Fluorescence; Optics; Computer science; Physics; Telecommunications","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":[0.0006034864,0.000845618,0.0005057279,0.0006407105,0.0005598209,0.0006913675,0.001464261,0.0006780938,0.00356672],"category_scores_gemma":[0.0004678165,0.0004886348,0.0003074105,0.0003970235,0.0003346603,0.0006124262,0.000530347,0.0006485487,0.001635099],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009442694,"about_ca_system_score_gemma":0.001355323,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008787317,"about_ca_topic_score_gemma":0.001476809,"domain_scores_codex":[0.9994643,0.0000423421,0.00003891474,0.0001856093,0.0002278568,0.00004094425],"domain_scores_gemma":[0.9996921,0.00005268699,0.00007660694,0.0000620143,0.00007863488,0.00003793411],"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":[0.00007769856,0.00003259341,0.000158541,0.00005039877,0.000005515386,0.00005499862,0.00002607728,0.0001252243,0.9852924,0.001048394,0.0004945374,0.01263362],"study_design_scores_gemma":[0.00002761558,0.0002407688,0.0004629618,0.000004650762,0.00001322962,0.000255672,0.000006159002,0.007489418,0.9814564,0.00007272365,0.009942904,0.00002747737],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4625414,0.002787194,0.5082909,0.0007877309,0.001255419,0.001623969,0.001186477,0.008632196,0.01289469],"genre_scores_gemma":[0.4906159,0.0009349874,0.4946297,0.0002572852,0.0001351017,0.0006717003,0.0007311598,0.0001599862,0.01186417],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00356672,"threshold_uncertainty_score":0.01193184,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03859458493286773,"score_gpt":0.2162408975700198,"score_spread":0.177646312637152,"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."}}