{"id":"W2080185046","doi":"10.1039/b918291h","title":"Microfluidic devices for cell based high throughput screening","year":2009,"lang":"en","type":"article","venue":"Lab on a Chip","topic":"3D Printing in Biomedical Research","field":"Engineering","cited_by":47,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Microfluidics; Throughput; High-throughput screening; Nanotechnology; Computer science; Computational biology; Engineering; Biology; Materials science; Bioinformatics; 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":[0.0002824344,0.000133958,0.0001407168,0.00009224111,0.00006732474,0.00004832313,0.0002588034,0.00009630295,0.0001006365],"category_scores_gemma":[0.00009461163,0.0001236481,0.00006080521,0.0001987058,0.00002620932,0.00004158693,0.00001681428,0.0002144917,0.0001001513],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003645442,"about_ca_system_score_gemma":0.00001958791,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001134016,"about_ca_topic_score_gemma":0.000001801765,"domain_scores_codex":[0.9989698,0.00002165637,0.000155671,0.0001974753,0.0002540623,0.000401358],"domain_scores_gemma":[0.9993609,0.0002380182,0.0000170304,0.0002407354,0.00002888214,0.0001144516],"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":[0.0003550478,0.0005966538,0.002775114,0.001587377,0.0001246357,0.00006296095,0.0004537138,0.003547969,0.4413406,0.006164188,0.1957039,0.3472879],"study_design_scores_gemma":[0.002822933,0.0006380128,0.03713511,0.0003544978,0.00002361269,0.000001845792,0.00002796973,0.04281296,0.5969883,0.001148132,0.3174082,0.0006384843],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.839609,0.003224541,0.1304148,0.003161424,0.0005539187,0.0008629524,0.00005008571,0.001290039,0.02083327],"genre_scores_gemma":[0.9811104,0.00006345671,0.01728552,0.00106572,0.0001878903,0.00001703462,0.00002056307,0.0000315752,0.0002178273],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3466494,"threshold_uncertainty_score":0.5042225,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0227492058424153,"score_gpt":0.2744102149526298,"score_spread":0.2516610091102146,"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."}}