{"id":"W4399355210","doi":"10.1007/s11517-024-03141-9","title":"Preparation of surgical meshes using self-regulating technology based on reaction-diffusion processes","year":2024,"lang":"en","type":"article","venue":"Medical & Biological Engineering & Computing","topic":"3D Printing in Biomedical Research","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"H2020 Marie Skłodowska-Curie Actions; Ministry of Culture, Multiculturalism and Status of Women, Government of Alberta; Horizon 2020 Framework Programme; National Institute of Food and Agriculture; European Commission; Budapesti Műszaki és Gazdaságtudományi Egyetem; U.S. Department of Agriculture","keywords":"Polygon mesh; Weaving; Computer science; Process (computing); Diffusion; Nanotechnology; Instrumentation (computer programming); Biocompatible material; Materials science; Mechanical engineering; Engineering; Biomedical engineering; Physics","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":[0.001043867,0.000284308,0.0003975327,0.0004649854,0.00008245212,0.00004602754,0.0003541399,0.0005609944,0.0001036323],"category_scores_gemma":[0.001875291,0.000221927,0.0001026493,0.001492094,0.0001407707,0.00005821409,0.0001633213,0.0008884462,0.00002527999],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00016288,"about_ca_system_score_gemma":0.0001215943,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007039479,"about_ca_topic_score_gemma":3.243791e-7,"domain_scores_codex":[0.9975229,0.00005217584,0.000633254,0.0004576675,0.0007791017,0.0005548865],"domain_scores_gemma":[0.9978414,0.001568873,0.00004437593,0.0002294649,0.00008999243,0.0002258499],"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":[0.00009575002,0.0005091907,0.006648532,0.006586993,0.0002908856,0.0004417876,0.0003691901,0.7014048,0.1392534,0.002984961,0.000253102,0.1411614],"study_design_scores_gemma":[0.0002105777,0.00014555,0.0005377529,0.001459239,0.00001044665,0.00005213383,0.000009983225,0.9894062,0.004231975,0.00005276397,0.00365641,0.0002269495],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9368092,0.0007770069,0.05838222,0.0002270764,0.0005540569,0.000213994,0.000002570583,0.002358719,0.0006751419],"genre_scores_gemma":[0.9878881,0.00007210661,0.01161903,0.00001224359,0.0003369332,0.00001051485,0.00001470808,0.00004242021,0.000003939471],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2880015,"threshold_uncertainty_score":0.904992,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01643879159483013,"score_gpt":0.308090198239862,"score_spread":0.2916514066450319,"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."}}