{"id":"W4243829483","doi":"10.1002/adfm.201604824","title":"Printed Microfluidics","year":2017,"lang":"en","type":"article","venue":"Advanced Functional Materials","topic":"Microfluidic and Capillary Electrophoresis Applications","field":"Engineering","cited_by":54,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Abbott Laboratories","keywords":"Microfluidics; Nanotechnology; Scalability; Materials science; 3D printing; Computer science","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":[0.0005581226,0.001010021,0.0008086875,0.001477666,0.0006582967,0.002237123,0.001587523,0.001582174,0.02000849],"category_scores_gemma":[0.00157275,0.0006368273,0.0006983581,0.0008691484,0.0008062545,0.001381806,0.001435637,0.001584705,0.01219946],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008801462,"about_ca_system_score_gemma":0.0006672874,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000346444,"about_ca_topic_score_gemma":0.0004724606,"domain_scores_codex":[0.9984648,0.0001241472,0.00009687996,0.0003849044,0.0008351173,0.0000941605],"domain_scores_gemma":[0.9993241,0.0002253988,0.000106834,0.0001256741,0.0001602086,0.00005779662],"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":[0.0001650539,0.0001714573,0.0005036949,0.003291587,0.0001248823,0.0008435464,0.0002563156,0.00279193,0.5313795,0.07922368,0.08506297,0.2961855],"study_design_scores_gemma":[0.00004373156,0.000169661,0.000377124,0.0002461284,0.0000545972,0.000958245,0.00002594164,0.00390627,0.3599793,0.008123326,0.6260078,0.0001078411],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02281077,0.117988,0.4888833,0.006264007,0.0268866,0.001046148,0.006494881,0.02001203,0.3096143],"genre_scores_gemma":[0.2620099,0.078123,0.4242974,0.007505012,0.005432878,0.001472721,0.004762642,0.001048547,0.2153479],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02000849,"threshold_uncertainty_score":0.06693512,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01061615255209111,"score_gpt":0.2174507117067468,"score_spread":0.2068345591546557,"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."}}