{"id":"W4210954067","doi":"10.1088/1361-6439/ac545f","title":"A perspective of active microfluidic platforms as an enabling tool for applications in other fields","year":2022,"lang":"en","type":"article","venue":"Journal of Micromechanics and Microengineering","topic":"Electrowetting and Microfluidic Technologies","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Microfluidics; Modular design; Nanotechnology; Digital microfluidics; Computer science; Microfluidic chip; Leverage (statistics); Engineering; Electrowetting; Materials science; Artificial intelligence; Electrical engineering","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":[0.002096795,0.0008291779,0.0006550876,0.001519887,0.0008242849,0.003536643,0.001293151,0.002625304,0.005001618],"category_scores_gemma":[0.001267468,0.0003232306,0.0007575109,0.0009720252,0.00233149,0.00534053,0.001972804,0.002554781,0.001705253],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007664918,"about_ca_system_score_gemma":0.001508676,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000424818,"about_ca_topic_score_gemma":0.0004553552,"domain_scores_codex":[0.9991768,0.0002672887,0.00004965254,0.0001270007,0.0002829843,0.00009620118],"domain_scores_gemma":[0.9990048,0.0005094542,0.00009486642,0.0000667428,0.0002290042,0.00009515674],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001067417,0.0001466061,0.0005180412,0.007315916,0.00004824502,0.00151397,0.0008842045,0.001392963,0.03744449,0.7160764,0.02072618,0.2138263],"study_design_scores_gemma":[0.00001045194,0.0002518721,0.0003736649,0.001807142,0.00004465256,0.001521248,0.0006614174,0.00131903,0.01277141,0.07720777,0.9039872,0.00004425258],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"review","genre_gemma":"empirical","genre_scores_codex":[0.006519584,0.8103774,0.06481246,0.02830167,0.004914693,0.00008245633,0.0001770915,0.0002365819,0.08457803],"genre_scores_gemma":[0.1087286,0.7940059,0.06407066,0.00869954,0.004638478,0.0002612784,0.0001600811,0.00007489366,0.01936061],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005001618,"threshold_uncertainty_score":0.01673204,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006370486360143758,"score_gpt":0.220713108443948,"score_spread":0.2143426220838042,"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."}}