{"id":"W3201740374","doi":"10.1002/admt.202100828","title":"E‐FLOAT: Extractable Floating Liquid Gel‐Based Organ‐on‐a‐Chip for Airway Tissue Modeling under Airflow","year":2021,"lang":"en","type":"article","venue":"Advanced Materials Technologies","topic":"3D Printing in Biomedical Research","field":"Engineering","cited_by":27,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Airflow; Organ-on-a-chip; Microfluidics; Float (project management); Airway; Biomedical engineering; Materials science; Lab-on-a-chip; Chip; Nanotechnology; Pathology; Computer science; Medicine; Engineering; 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":[0.0002463442,0.0004931566,0.0002352221,0.0003476806,0.0001723474,0.0005322534,0.0005796371,0.0007533341,0.001026354],"category_scores_gemma":[0.0001694129,0.0002156486,0.0004103941,0.0001200215,0.0003214795,0.0004056859,0.0004309252,0.0005148533,0.0005354357],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002512285,"about_ca_system_score_gemma":0.0002241978,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004265491,"about_ca_topic_score_gemma":0.0007702745,"domain_scores_codex":[0.9998562,0.00001477065,0.000009325564,0.00004041279,0.00005825016,0.00002094182],"domain_scores_gemma":[0.9998584,0.00004222396,0.0000396536,0.0000240036,0.00001894054,0.00001678836],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001890024,0.00002461133,0.00009862961,0.00006693398,0.000006791523,0.00007904595,0.00001888698,0.001719818,0.9929215,0.0003617149,0.0002598965,0.00442326],"study_design_scores_gemma":[0.000007208081,0.0001248453,0.0006178677,0.00001098788,0.00001270527,0.0001444917,0.00001351019,0.02024736,0.9728472,0.0001153391,0.005833621,0.0000249057],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4896535,0.00375062,0.4905382,0.0004915587,0.0003245959,0.0002330466,0.00163905,0.003935676,0.009433821],"genre_scores_gemma":[0.7505944,0.00189342,0.2381477,0.0003319986,0.00005369517,0.000323494,0.0008767118,0.0002277167,0.007550844],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001026354,"threshold_uncertainty_score":0.003433526,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02510001162361984,"score_gpt":0.2945523130688353,"score_spread":0.2694523014452154,"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."}}