{"id":"W2008326670","doi":"10.1007/s12289-014-1205-8","title":"Titanium based cranial reconstruction using incremental sheet forming","year":2014,"lang":"en","type":"article","venue":"International Journal of Material Forming","topic":"Metal Forming Simulation Techniques","field":"Engineering","cited_by":71,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Engineering and Physical Sciences Research Council; University College London Hospitals NHS Foundation Trust; Queen's University Belfast; Queen's University; University College London","keywords":"Incremental sheet forming; Finite element method; Process (computing); Materials science; Deformation (meteorology); Forming processes; Computer science; Structural engineering; Composite material; 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":[0.0005590571,0.0001462401,0.0002067605,0.0003428554,0.00005708302,0.000121231,0.0002768951,0.00008113786,0.000284717],"category_scores_gemma":[0.00008361464,0.0001434772,0.0001161401,0.00006233753,0.00002524979,0.0008855739,0.00003403069,0.0001273698,0.000006311962],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002894288,"about_ca_system_score_gemma":0.00002873215,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001597049,"about_ca_topic_score_gemma":0.000002905797,"domain_scores_codex":[0.9986155,0.00002799707,0.0006708739,0.00008306505,0.0004377566,0.0001648049],"domain_scores_gemma":[0.9993143,0.00004199999,0.0002666941,0.00008236628,0.000233469,0.00006118723],"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":[0.0002408521,0.00002813943,0.0007741855,0.00005930295,0.0001838432,0.00001915252,0.0001087755,0.01195909,0.9188602,0.001692795,0.0001270624,0.06594661],"study_design_scores_gemma":[0.001102385,0.0001273744,0.000188788,0.0003625532,0.00005280142,0.0007431602,0.00005074405,0.1992171,0.791238,0.003068655,0.003551592,0.0002969096],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9165936,0.000007680555,0.07583748,0.00002235326,0.006345188,0.00006898979,0.00001289102,0.0001013711,0.001010428],"genre_scores_gemma":[0.9550875,0.00000483849,0.04351782,0.0000385698,0.001302884,0.000001313915,0.000008648036,0.0000318603,0.000006531196],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.187258,"threshold_uncertainty_score":0.5850829,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01359704105179551,"score_gpt":0.2576372574482279,"score_spread":0.2440402163964323,"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."}}