{"id":"W4286267536","doi":"10.1016/j.cell.2022.06.015","title":"Tissue bioprinting for biology and medicine","year":2022,"lang":"en","type":"article","venue":"Cell","topic":"3D Printing in Biomedical Research","field":"Engineering","cited_by":24,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Victoria","funders":"National Institute of Arthritis and Musculoskeletal and Skin Diseases; National Institute of General Medical Sciences","keywords":"Biology; 3D bioprinting; Regenerative medicine; Tissue engineering; Computational biology; Engineering ethics; Stem cell; Cell biology; Engineering; Genetics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003514131,0.00004644398,0.000077197,0.0000635581,0.00008545577,0.000003859949,0.000111165,0.00002391988,0.0004700581],"category_scores_gemma":[0.00008935216,0.00004376474,0.000008502403,0.00009482608,0.00006513729,0.000007701265,0.0001296822,0.0001327036,0.000009598307],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002141254,"about_ca_system_score_gemma":0.000005282868,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001255268,"about_ca_topic_score_gemma":4.154644e-7,"domain_scores_codex":[0.9995448,0.000014315,0.0000878557,0.0001012404,0.00007058568,0.0001811892],"domain_scores_gemma":[0.9996581,0.0001832457,0.000008314794,0.00008616844,0.000009973531,0.00005418357],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000006395636,0.00001571273,0.0009858412,0.0003186344,0.00001525622,0.000004989994,0.0003740942,0.0003582519,0.8684044,0.001784365,0.01347887,0.1142532],"study_design_scores_gemma":[0.0004945676,0.0001699589,0.0005289821,0.000006968678,0.000005091261,0.000004319006,0.0001360356,0.01699481,0.06979274,0.002248792,0.909503,0.0001146799],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8958315,0.004317218,0.04133765,0.002249788,0.001651926,0.0006738071,0.00002698324,0.0005368483,0.05337423],"genre_scores_gemma":[0.9971793,0.00003888689,0.001679072,0.00004599213,0.0001298941,0.0000503868,0.000009651101,0.0000158589,0.0008509366],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8960242,"threshold_uncertainty_score":0.5146807,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02311367427880892,"score_gpt":0.3188549089968623,"score_spread":0.2957412347180534,"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."}}