{"id":"W2606470863","doi":"10.1103/physreve.95.043105","title":"Parallelization of microfluidic flow-focusing devices","year":2017,"lang":"en","type":"article","venue":"Physical review. E","topic":"Innovative Microfluidic and Catalytic Techniques Innovation","field":"Engineering","cited_by":29,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Materials Research Science and Engineering Center, Harvard University; National Science Foundation","keywords":"Microfluidics; Drop (telecommunication); Robustness (evolution); Flow focusing; Flow (mathematics); Flow control (data); Volumetric flow rate; Materials science; Mechanics; Two-phase flow; Fluid dynamics; Computer science; Nanotechnology; Physics; Chemistry; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000158575,0.0001208838,0.0002665145,0.00003284876,0.00008555469,0.00002102243,0.000251417,0.00002741679,0.00001904427],"category_scores_gemma":[0.00008422518,0.000108312,0.00006397059,0.0001360756,0.00007566904,0.0002146261,0.00004875625,0.00009398314,0.00002823774],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000302341,"about_ca_system_score_gemma":0.00001181729,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000050343,"about_ca_topic_score_gemma":1.074013e-7,"domain_scores_codex":[0.9993577,0.00001031352,0.0002677363,0.0001154,0.0001207603,0.0001280795],"domain_scores_gemma":[0.9992919,0.00001373655,0.0001271568,0.0004080992,0.0001407839,0.00001829932],"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":[0.000002791692,0.00004270575,0.0004271374,0.002085227,0.00003984956,0.000001363348,0.00007008509,0.000005064284,0.8329439,0.02949511,0.02139352,0.1134933],"study_design_scores_gemma":[0.0001334903,0.00002216177,0.00230057,0.001554388,0.00004705948,0.000002155682,0.0000039123,0.001551354,0.9356595,0.0048024,0.05370073,0.0002222902],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2934455,0.07137352,0.602092,0.0007418332,0.0004760034,0.0009253793,0.00003506227,0.0005533563,0.03035732],"genre_scores_gemma":[0.9930165,0.005892271,0.0007314991,0.0001392157,0.000134439,0.00001617681,0.00002999395,0.00002094802,0.00001895689],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.699571,"threshold_uncertainty_score":0.4416834,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02074644614858331,"score_gpt":0.3170194336930187,"score_spread":0.2962729875444354,"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."}}