{"id":"W1994521364","doi":"10.1089/ten.2006.0253","title":"Design and Fabrication of Sub-mm-Sized Modules Containing Encapsulated Cells for Modular Tissue Engineering","year":2007,"lang":"en","type":"article","venue":"Tissue Engineering","topic":"3D Printing in Biomedical Research","field":"Engineering","cited_by":53,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"National Institute of Biomedical Imaging and Bioengineering; National Institutes of Natural Sciences; Canadian Institutes of Health Research; National Institutes of Health","keywords":"Modular design; Scaffold; Tissue engineering; Umbilical vein; Fabrication; Biomedical engineering; Construct (python library); Process (computing); Self-healing hydrogels; Layer (electronics); Materials science; Nanotechnology; Computer science; Chemistry; Engineering; In vitro","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001251526,0.0002912506,0.0003990926,0.0003899452,0.00004405653,0.00003748338,0.000243203,0.0002097038,0.00001606003],"category_scores_gemma":[0.000517396,0.0003391257,0.00004144923,0.0004183664,0.00003731973,0.0001421976,0.00006352922,0.0002754752,0.000008309956],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001116796,"about_ca_system_score_gemma":0.00001577578,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001363703,"about_ca_topic_score_gemma":4.847415e-7,"domain_scores_codex":[0.998168,0.00001547596,0.0005278821,0.0002969475,0.000323915,0.0006677678],"domain_scores_gemma":[0.9985232,0.0007741838,0.0000536542,0.0002935934,0.0001243106,0.0002311079],"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":[0.00001242319,0.00000731881,0.000007153423,0.0003186489,0.00004032697,0.000005634207,0.0001700455,0.3422121,0.6444958,0.0001323236,0.00002531108,0.01257294],"study_design_scores_gemma":[0.000327788,0.00005880176,0.0005360731,0.00008720772,0.00001250814,0.000004082949,0.000009774747,0.4677052,0.52964,0.00001651103,0.00141174,0.0001903636],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2908033,0.0009979928,0.70697,0.00001060827,0.0002697897,0.0005233861,0.000004885912,0.0003983067,0.00002179401],"genre_scores_gemma":[0.8632868,0.00008707212,0.1362884,0.000002285693,0.0001396964,0.00004760312,0.00001287784,0.0001060167,0.0000292418],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5724835,"threshold_uncertainty_score":0.9999061,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01500497618271345,"score_gpt":0.2555475989369388,"score_spread":0.2405426227542253,"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."}}