{"id":"W4224290886","doi":"10.1016/j.addma.2022.102808","title":"A kinetic model for predicting imperfections in bioink photopolymerization during visible-light stereolithography printing","year":2022,"lang":"en","type":"article","venue":"Additive manufacturing","topic":"3D Printing in Biomedical Research","field":"Engineering","cited_by":15,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto; University of Calgary; University of British Columbia, Okanagan Campus; University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; Science and Engineering Research Board; Compute Canada","keywords":"Photopolymer; Stereolithography; Materials science; Biofabrication; Photoinitiator; Polymerization; Visible spectrum; Light intensity; Self-healing hydrogels; Quenching (fluorescence); Nanotechnology; Polymer; Biomedical engineering; Optoelectronics; Composite material; Polymer chemistry; Optics; Tissue engineering; Fluorescence; Monomer","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005312448,0.0006267934,0.0006218597,0.0005437059,0.0003653872,0.0007437139,0.0007667486,0.001463062,0.001145995],"category_scores_gemma":[0.00191911,0.0006843722,0.0005884885,0.0004591619,0.0005385317,0.001005789,0.0003018343,0.0007333601,0.0003961324],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001365254,"about_ca_system_score_gemma":0.001165568,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01348293,"about_ca_topic_score_gemma":0.006856293,"domain_scores_codex":[0.99984,0.00001395402,0.00001158784,0.00003613945,0.0000632557,0.00003503274],"domain_scores_gemma":[0.9990945,0.0005838138,0.0001263653,0.00005430541,0.0001133821,0.00002771026],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002441631,0.00002602577,0.0005005018,0.00003908791,0.000005934775,0.00003877614,0.00001769193,0.9821989,0.01225885,0.00110767,0.00007596651,0.003706168],"study_design_scores_gemma":[0.000001167448,0.00000546442,0.0001146959,0.000001206153,0.00000189098,0.000005608299,0.000001752124,0.9969755,0.00270675,0.0001393191,0.00004363295,0.000003113328],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3850662,0.000928006,0.6039685,0.0003773284,0.00009208698,0.0001097463,0.000404251,0.001239481,0.007814374],"genre_scores_gemma":[0.9758484,0.0004059202,0.01854568,0.00004185284,0.000008312139,0.00007868031,0.000147306,0.0001176052,0.004806283],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01348293,"threshold_uncertainty_score":0.02680892,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01078705463631943,"score_gpt":0.2405070390641905,"score_spread":0.2297199844278711,"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."}}