{"id":"W4310460856","doi":"10.1039/d2lc00764a","title":"Integrating machine learning and digital microfluidics for screening experimental conditions","year":2022,"lang":"en","type":"article","venue":"Lab on a Chip","topic":"Electrowetting and Microfluidic Technologies","field":"Engineering","cited_by":37,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; Montreal Neurological Institute and Hospital; University of Toronto; University of New Brunswick; Concordia University","funders":"Fonds de recherche du Québec – Nature et technologies; Natural Sciences and Engineering Research Council of Canada; Canada Foundation for Innovation","keywords":"Microfluidics; Digital microfluidics; Set (abstract data type); Computer science; Yield (engineering); Biochemical engineering; Machine learning; Ideal (ethics); Artificial intelligence; Biological system; Nanotechnology; Engineering; Materials science; Biology","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":[0.00007830663,0.0001171121,0.0001051178,0.0000861211,0.0003754306,0.00005661495,0.0000831256,0.00003126835,0.0000240799],"category_scores_gemma":[0.00005419246,0.000124615,0.00003723052,0.00009702505,0.00003034409,0.00005391649,0.00007121786,0.0003463399,0.000001663654],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004908258,"about_ca_system_score_gemma":0.000005442635,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005073491,"about_ca_topic_score_gemma":4.575487e-7,"domain_scores_codex":[0.9994584,0.00001133603,0.0001131711,0.0001438002,0.00006610009,0.0002072041],"domain_scores_gemma":[0.9997897,0.00008594621,0.00002095214,0.00007100347,0.000006849855,0.00002557753],"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":[0.00001614165,0.00002674621,0.0007523928,0.00001694347,0.00003392415,0.000004089532,0.0004861822,0.0001207289,0.9753062,0.002446527,0.002586994,0.0182032],"study_design_scores_gemma":[0.0007699465,0.0005247468,0.00004594134,0.00003450172,0.00001028162,0.00006678355,0.003414525,0.004145252,0.8805219,0.0002814874,0.1098812,0.0003034487],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9869576,0.006988863,0.003389193,0.00013817,0.00009461003,0.0001698642,0.000138944,0.001087328,0.001035483],"genre_scores_gemma":[0.9982941,0.00004163394,0.001035359,0.0000628512,0.00002475211,0.0001003137,0.0001192351,0.00003543441,0.0002863338],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1072942,"threshold_uncertainty_score":0.5081651,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00900854229006134,"score_gpt":0.2310487274976656,"score_spread":0.2220401852076043,"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."}}