{"id":"W3002862926","doi":"10.1016/j.ohx.2020.e00096","title":"µPump: An open-source pressure pump for precision fluid handling in microfluidics","year":2020,"lang":"en","type":"article","venue":"HardwareX","topic":"Microfluidic and Capillary Electrophoresis Applications","field":"Engineering","cited_by":34,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Waterloo Institute for Nanotechnology, University of Waterloo; Natural Sciences and Engineering Research Council of Canada; University of Waterloo; Ontario Centres of Excellence","keywords":"Microfluidics; Software; Mechatronics; Settling time; Computer science; Flow control (data); Engineering; Control engineering; Materials science; Nanotechnology; Operating system; Telecommunications","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001694689,0.0001996821,0.000271783,0.00005458145,0.0001071601,0.0001311931,0.0006334214,0.000129746,0.0001361719],"category_scores_gemma":[0.00005590411,0.0002130254,0.00005328534,0.0002579782,0.0000190846,0.0001854661,0.0001098327,0.0001683899,0.00004855747],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003637655,"about_ca_system_score_gemma":0.00003817199,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001994763,"about_ca_topic_score_gemma":0.000003906624,"domain_scores_codex":[0.9987929,0.00002984725,0.0003194492,0.0003856253,0.0001252695,0.0003469276],"domain_scores_gemma":[0.999334,0.00006491662,0.00002961652,0.0003475167,0.00004774203,0.0001761502],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00005935339,0.00003639444,0.0001380751,0.00007736444,0.00002879244,0.000001513689,0.0006572659,0.0006931886,0.8640119,0.0002334897,0.1295486,0.004514087],"study_design_scores_gemma":[0.0005397742,0.00009325589,0.0001651079,0.00002542311,0.00002399785,0.000002643425,0.0000649092,0.01546852,0.3023549,0.00008873013,0.680957,0.0002158152],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6488876,0.1168461,0.2259411,0.001143009,0.000272024,0.003653412,0.0002311003,0.00100881,0.002016879],"genre_scores_gemma":[0.9798038,0.01755275,0.0007945591,0.0005869751,0.0002828421,0.0003493537,0.0001988059,0.0001316518,0.0002992451],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.561657,"threshold_uncertainty_score":0.8686925,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0217012297468246,"score_gpt":0.2454767752949617,"score_spread":0.2237755455481371,"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."}}