{"id":"W4200527289","doi":"10.1002/admt.202170070","title":"E‐FLOAT: Extractable Floating Liquid Gel‐Based Organ‐on‐a‐Chip for Airway Tissue Modeling under Airflow (Adv. Mater. Technol. 12/2021)","year":2021,"lang":"en","type":"article","venue":"Advanced Materials Technologies","topic":"3D Printing in Biomedical Research","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Microfluidics; Airflow; Microfluidic chip; Organ-on-a-chip; Airway; Float (project management); Chip; Biomedical engineering; Materials science; Staining; Nanotechnology; Pathology; Medicine; Engineering; Surgery; Mechanical engineering","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":[0.0002867001,0.0004672572,0.0002080578,0.000390753,0.0002073285,0.0003408588,0.0007110928,0.0007140076,0.002303438],"category_scores_gemma":[0.000133219,0.0003251716,0.0003146482,0.0001042357,0.0002957704,0.0006710818,0.0003582493,0.00053176,0.001246317],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00021686,"about_ca_system_score_gemma":0.0001463425,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004501491,"about_ca_topic_score_gemma":0.0009131056,"domain_scores_codex":[0.9998971,0.000008734153,0.000006520228,0.00003319218,0.000037398,0.00001693662],"domain_scores_gemma":[0.9999336,0.00002390242,0.0000166598,0.00000760674,0.000008487849,0.000009698746],"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":[0.00002827157,0.00003284001,0.00004657409,0.00005785751,0.000005090022,0.00006226094,0.00002092575,0.0002799911,0.9944956,0.0002041841,0.0004556972,0.00431079],"study_design_scores_gemma":[0.0000130105,0.0001092476,0.0004129727,0.000006307566,0.000007044817,0.0001152064,0.000007927834,0.004169663,0.9902762,0.00006082479,0.004805562,0.00001601448],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5307726,0.00928227,0.4366834,0.001340985,0.0008477918,0.0004869028,0.003384591,0.005161988,0.01203947],"genre_scores_gemma":[0.6540012,0.005700486,0.3013922,0.0008910982,0.000195355,0.0005114842,0.002705477,0.0004133499,0.03418947],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002303438,"threshold_uncertainty_score":0.007705808,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02242934353237005,"score_gpt":0.2867474009743247,"score_spread":0.2643180574419547,"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."}}