{"id":"W2751903978","doi":"10.1039/c7lc00826k","title":"Image-based feedback and analysis system for digital microfluidics","year":2017,"lang":"en","type":"article","venue":"Lab on a Chip","topic":"Electrowetting and Microfluidic Technologies","field":"Engineering","cited_by":66,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada; Concordia University","keywords":"Microfluidics; Digital microfluidics; Computer science; Digital imaging; Digital image analysis; Digital image; Engineering; Nanotechnology; Image (mathematics); Artificial intelligence; Computer vision; Image processing; Materials science; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0000810916,0.0001347534,0.0002009527,0.0001173132,0.0001851947,0.0002822088,0.0002146246,0.00008953341,0.000001259559],"category_scores_gemma":[0.00008108903,0.0001241404,0.0000897772,0.00007951992,0.00006528848,0.00007840137,0.00002568892,0.00009665197,0.00001247307],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004197114,"about_ca_system_score_gemma":0.00000854168,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006605007,"about_ca_topic_score_gemma":0.000001813849,"domain_scores_codex":[0.9994222,0.000003687165,0.0001172553,0.0001736631,0.00005531785,0.0002279028],"domain_scores_gemma":[0.9993888,0.00004971589,0.0000403802,0.0004610023,0.00002415976,0.00003598015],"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":[0.00007741548,0.00005063336,0.009474284,0.0008515558,0.0009691651,0.00002126143,0.0001165893,0.00003993801,0.8981356,0.001906315,0.01294557,0.07541168],"study_design_scores_gemma":[0.001018394,0.0001173937,0.004727264,0.000110433,0.000250583,0.000005465043,0.00008765349,0.00359585,0.9794105,0.0001109425,0.01021023,0.0003553027],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9825974,0.001501017,0.01171912,0.0002936518,0.0001179802,0.0001759422,0.000137347,0.001099835,0.002357676],"genre_scores_gemma":[0.9984736,0.0000456274,0.0012343,0.00002199242,0.00003195333,0.00002056344,0.00002184649,0.00002554241,0.0001245914],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0812749,"threshold_uncertainty_score":0.5062298,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008146915618690206,"score_gpt":0.2148764499220505,"score_spread":0.2067295343033603,"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."}}