{"id":"W2967496489","doi":"10.1109/mnano.2019.2927773","title":"Drug Discovery Applications: A Customized Digital Microfluidic Biochip Architecture/CAD Flow","year":2019,"lang":"en","type":"article","venue":"IEEE Nanotechnology Magazine","topic":"Electrowetting and Microfluidic Technologies","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Biochip; Drug discovery; Computer science; Microfluidics; Throughput; Embedded system; Nanotechnology; Bioinformatics; Materials 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":[0.0002183642,0.0005192024,0.0003120976,0.0004859892,0.0002499829,0.0004975985,0.0006645093,0.0005122488,0.00204031],"category_scores_gemma":[0.0002759421,0.0002883531,0.000290493,0.000325618,0.0002236713,0.0005058125,0.0004458532,0.0005159198,0.0008090517],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004066169,"about_ca_system_score_gemma":0.0006328084,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008227607,"about_ca_topic_score_gemma":0.00109138,"domain_scores_codex":[0.9997935,0.00001563989,0.00001721264,0.00005566046,0.00009112446,0.0000268815],"domain_scores_gemma":[0.9999275,0.00001222613,0.00001166972,0.00001312883,0.00002238168,0.00001300101],"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":[0.0001032727,0.0001165253,0.0003707266,0.0002319866,0.00001267331,0.0001467368,0.00002692086,0.003225366,0.8963017,0.00406136,0.003248122,0.09215454],"study_design_scores_gemma":[0.0001094547,0.0003627778,0.001462759,0.0000323819,0.00003958457,0.000639468,0.00001167477,0.04941045,0.8660171,0.0009884721,0.08085948,0.00006644936],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1325542,0.004385098,0.8235345,0.001848959,0.0009306853,0.0013036,0.001342845,0.009114756,0.02498525],"genre_scores_gemma":[0.2947397,0.002146146,0.6924587,0.001250736,0.0001907981,0.0005835661,0.0008128637,0.0001600103,0.007657643],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.00204031,"threshold_uncertainty_score":0.006825447,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.003207752927914176,"score_gpt":0.1855719563192056,"score_spread":0.1823642033912914,"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."}}