{"id":"W2059413563","doi":"10.1089/ten.tec.2010.0241","title":"Simple Modular Bioreactors for Tissue Engineering: A System for Characterization of Oxygen Gradients, Human Mesenchymal Stem Cell Differentiation, and Prevascularization","year":2010,"lang":"en","type":"article","venue":"Tissue Engineering Part C Methods","topic":"Electrospun Nanofibers in Biomedical Applications","field":"Materials Science","cited_by":33,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Institutes of Health; National Institute of Biomedical Imaging and Bioengineering; McMaster University","keywords":"Tissue engineering; Mesenchymal stem cell; Bioreactor; Oxygen transport; Modular design; Scaffold; Biomedical engineering; Stem cell; Biochemical engineering; Decellularization; Chondrogenesis; Chemistry; Cell biology; Oxygen; Computer science; Biology; Engineering","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009358166,0.0002404221,0.0003914939,0.0001626153,0.0001272413,0.00005510398,0.0002366378,0.0002003498,0.00001744477],"category_scores_gemma":[0.00007642759,0.0002438588,0.0000602917,0.0002559096,0.00003895783,0.0001394638,0.00005084672,0.00009784888,0.000001657201],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004624429,"about_ca_system_score_gemma":0.00001924178,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007263704,"about_ca_topic_score_gemma":5.922531e-7,"domain_scores_codex":[0.9984497,0.00005091927,0.000534626,0.0004229969,0.0001953498,0.0003464062],"domain_scores_gemma":[0.9988461,0.0002284493,0.0002292087,0.000394599,0.0001532021,0.0001484889],"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":[0.000004741326,0.00004025851,0.00003896248,0.0008776158,0.00001869926,9.625208e-8,0.0001451724,0.0001662067,0.9911273,0.006499212,0.00001190584,0.001069797],"study_design_scores_gemma":[0.0003871387,0.0001033482,0.001705292,0.00003674492,0.00008495695,0.00000281929,0.000004973657,0.02446551,0.9565212,0.00006343611,0.01639215,0.0002324993],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4008289,0.00003791023,0.5975339,0.00001472389,0.0004327807,0.0009187692,0.00009613421,0.000134636,0.000002213153],"genre_scores_gemma":[0.6439786,0.000005693408,0.3542528,0.000002958871,0.0002748467,0.0007998824,0.0004759665,0.00007126309,0.000137976],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.2432811,"threshold_uncertainty_score":0.9944274,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01153171160572727,"score_gpt":0.2816520378729205,"score_spread":0.2701203262671933,"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."}}