{"id":"W4389142446","doi":"10.26434/chemrxiv-2023-w7vzl","title":"A microfluidic bio membrane for flow rate metering","year":2023,"lang":"en","type":"preprint","venue":"ChemRxiv","topic":"Microfluidic and Bio-sensing Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Microfluidics; Volumetric flow rate; Membrane; Metering mode; Materials science; PID controller; Response time; Flow measurement; Flow (mathematics); Control theory (sociology); Computer science; Temperature control; Engineering; Control engineering; Chemistry; Nanotechnology; Mechanical engineering; Physics; Mechanics; Control (management)","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":[0.0007204723,0.0007385883,0.0003266082,0.0004279962,0.0003588641,0.0005437191,0.0006580866,0.0009323739,0.0009112912],"category_scores_gemma":[0.0006560989,0.0002590441,0.0003231341,0.0002238663,0.0003270108,0.0006548951,0.0003729723,0.0006860786,0.0008360422],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007073709,"about_ca_system_score_gemma":0.0006059948,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003749454,"about_ca_topic_score_gemma":0.000338777,"domain_scores_codex":[0.9993352,0.00009648714,0.00004773236,0.0001821224,0.0002923178,0.00004599578],"domain_scores_gemma":[0.9997301,0.00007370048,0.00007357415,0.0000387896,0.00006110584,0.00002272137],"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":[0.00002178595,0.00001389932,0.0000951338,0.00007532381,0.000003390679,0.00003172656,0.000008692762,0.00009839173,0.9908517,0.0006714444,0.0002219382,0.007906578],"study_design_scores_gemma":[0.000008268988,0.00008483414,0.0003609397,0.000009947173,0.000008266512,0.000245916,0.000004851176,0.001521674,0.9855436,0.0001025888,0.01209616,0.00001302648],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2786621,0.01560669,0.6853103,0.002731782,0.001532345,0.0008147854,0.001380456,0.003309383,0.01065214],"genre_scores_gemma":[0.4582711,0.005435432,0.5271894,0.0006330348,0.0002493204,0.0007057402,0.0006346207,0.0001409064,0.00674043],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0009323739,"threshold_uncertainty_score":0.005132377,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04241977839608474,"score_gpt":0.2410518117169198,"score_spread":0.198632033320835,"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."}}