{"id":"W2111123160","doi":"10.1039/c5lc00314h","title":"Label-free high-throughput detection and content sensing of individual droplets in microfluidic systems","year":2015,"lang":"en","type":"article","venue":"Lab on a Chip","topic":"Innovative Microfluidic and Catalytic Techniques Innovation","field":"Engineering","cited_by":65,"is_retracted":false,"has_abstract":true,"ca_institutions":"Regional Municipality of Waterloo; University of Waterloo","funders":"University of Toronto","keywords":"Microfluidics; Throughput; Nanotechnology; Content (measure theory); Process engineering; Computer science; Materials science; Engineering; Telecommunications; Mathematics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004588379,0.0004389623,0.0005505581,0.0003653482,0.000144369,0.000454225,0.0008326176,0.0005960077,0.0003638047],"category_scores_gemma":[0.00047757,0.0003210929,0.0003985849,0.0002146741,0.000323912,0.0008098242,0.0006435804,0.0004010928,0.0003798811],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003533832,"about_ca_system_score_gemma":0.0001404165,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001316876,"about_ca_topic_score_gemma":0.0002414187,"domain_scores_codex":[0.9994875,0.0000695724,0.00004491589,0.0001437004,0.0002191168,0.00003512139],"domain_scores_gemma":[0.9997144,0.0001140815,0.0000781054,0.00003663604,0.00003929188,0.00001742147],"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":[0.00001836492,0.00001039333,0.00008555854,0.00005217922,0.000003908559,0.00001945377,0.00001246821,0.0001530062,0.9962624,0.0001141437,0.00002600529,0.003242174],"study_design_scores_gemma":[0.000009702002,0.0001100697,0.0003650944,0.000003369218,0.0000111286,0.00009504268,0.000005876615,0.003617026,0.9943002,0.0000732937,0.001398036,0.00001104722],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5958334,0.006429599,0.3928064,0.0003263974,0.0002764942,0.0004071475,0.0004841342,0.001486555,0.001949974],"genre_scores_gemma":[0.6787909,0.002885951,0.3152251,0.0002237781,0.0001700015,0.0002779183,0.0003035429,0.00008172583,0.002041133],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0008326176,"threshold_uncertainty_score":0.002563953,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04116875336529285,"score_gpt":0.2362744242509699,"score_spread":0.195105670885677,"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."}}