{"id":"W2995759622","doi":"10.1039/c9lc01042d","title":"When robotics met fluidics","year":2019,"lang":"en","type":"article","venue":"Lab on a Chip","topic":"Innovative Microfluidic and Catalytic Techniques Innovation","field":"Engineering","cited_by":39,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; Toronto Public Health","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Robotics; Fluidics; Artificial intelligence; Engineering; Aeronautics; Computer science; Robot; Aerospace 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00698916,0.001165276,0.001054288,0.001842422,0.003782847,0.0113461,0.001367782,0.00689217,0.01706512],"category_scores_gemma":[0.01455996,0.0006286527,0.0007138974,0.0008716554,0.01976725,0.01831494,0.007295675,0.007977497,0.007896634],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.005234259,"about_ca_system_score_gemma":0.005158874,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004577068,"about_ca_topic_score_gemma":0.003173544,"domain_scores_codex":[0.9932644,0.002026034,0.0003310751,0.001386021,0.002088129,0.0009043243],"domain_scores_gemma":[0.9970027,0.0009142053,0.0002964906,0.0005013282,0.0007661036,0.0005191186],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00007072806,0.00003543478,0.0009746897,0.0003542787,0.0000348197,0.0002704531,0.001655875,0.00117345,0.001355554,0.8624102,0.0611348,0.07052962],"study_design_scores_gemma":[0.00001844489,0.00006215867,0.0003271441,0.0004510906,0.00001340568,0.0002379496,0.001386565,0.0007294125,0.0008498461,0.3120624,0.6838107,0.00005087613],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.01011596,0.05288997,0.09200832,0.4004903,0.02705417,0.0001582283,0.0003104365,0.001078441,0.4158942],"genre_scores_gemma":[0.4335764,0.05883333,0.07626885,0.1505693,0.01772536,0.0008174297,0.0004142339,0.001326043,0.260469],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01706512,"threshold_uncertainty_score":0.05708849,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01236378589101173,"score_gpt":0.2239458846371616,"score_spread":0.2115820987461499,"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."}}