{"id":"W2921218192","doi":"10.1039/c9lc00128j","title":"Semi-automated on-demand control of individual droplets with a sample application to a drug screening assay","year":2019,"lang":"en","type":"article","venue":"Lab on a Chip","topic":"Innovative Microfluidic and Catalytic Techniques Innovation","field":"Engineering","cited_by":31,"is_retracted":false,"has_abstract":true,"ca_institutions":"Regional Municipality of Waterloo; University of Waterloo","funders":"Waterloo Institute for Nanotechnology, University of Waterloo; Natural Sciences and Engineering Research Council of Canada; Ontario Centres of Excellence","keywords":"Microfluidics; Repeatability; On demand; Throughput; Sorting; Computer science; Nanotechnology; Tracking (education); Mixing (physics); Flow control (data); Fabrication; Sample (material); Drug delivery; Simulation; Biological system; Biomedical engineering; Materials science; Engineering; Chemistry; Chromatography; Algorithm; Physics","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":[0.0009517841,0.0005085417,0.0006233764,0.0004231439,0.0003633444,0.001124886,0.001014878,0.0005223931,0.002088034],"category_scores_gemma":[0.001250094,0.0003968834,0.0004454209,0.0003135256,0.0005200335,0.0006148902,0.0008618982,0.0008086617,0.001067958],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004206392,"about_ca_system_score_gemma":0.0007095368,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005543557,"about_ca_topic_score_gemma":0.0009974255,"domain_scores_codex":[0.9989544,0.0001259095,0.00008122943,0.0002437191,0.0005300569,0.00006467941],"domain_scores_gemma":[0.9988962,0.0003512362,0.0001564745,0.0003304586,0.0002034404,0.00006223765],"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":[0.00009643125,0.00006270582,0.0003503796,0.00005796913,0.00001052986,0.00003572134,0.00004186164,0.001089359,0.9729359,0.000521856,0.0003233684,0.02447396],"study_design_scores_gemma":[0.00002495904,0.0002084725,0.00101849,0.000006002911,0.00001242402,0.0001200265,0.00001247159,0.03749096,0.9563795,0.0002683992,0.004423921,0.00003435569],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2189029,0.0005800158,0.7697322,0.0002954221,0.0002152911,0.0009487331,0.0005853855,0.005763551,0.002976433],"genre_scores_gemma":[0.4454243,0.0004414559,0.5486298,0.0002456848,0.00007211396,0.0005241524,0.0004738005,0.0003724221,0.003816344],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002088034,"threshold_uncertainty_score":0.006985188,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006415290463348535,"score_gpt":0.2228566958483206,"score_spread":0.2164414053849721,"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."}}