{"id":"W3108985346","doi":"10.4325/seikeikakou.27.359","title":"Development of Microscopic Neurosurgical Training Model using Additive Manufacturing Technology","year":2015,"lang":"en","type":"article","venue":"Seikei-Kakou","topic":"Anatomy and Medical Technology","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Nexen (Canada)","funders":"","keywords":"Materials science; Training (meteorology); Manufacturing engineering; Nanotechnology; Biomedical engineering; Engineering","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":[0.0001325375,0.0001810872,0.0003148307,0.0002459705,0.00005112696,0.000005860092,0.0002222567,0.0003096721,0.0000189498],"category_scores_gemma":[0.00005007286,0.0001815975,0.00003999885,0.0001935643,0.0001692279,0.00006807708,0.0001033355,0.0003793678,0.00001471408],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001052305,"about_ca_system_score_gemma":0.0001487541,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001857472,"about_ca_topic_score_gemma":0.000004152387,"domain_scores_codex":[0.9988593,0.000009871822,0.0003455401,0.0002136374,0.0001669508,0.0004046485],"domain_scores_gemma":[0.9995722,0.00002136484,0.00004931438,0.0001877496,0.00003346437,0.000135904],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00006587864,0.0001861129,0.0001669573,0.0003120354,0.0003389837,0.0003045615,0.01044666,0.0641321,0.1044402,0.008585555,0.001074863,0.8099461],"study_design_scores_gemma":[0.001168775,0.00005611728,0.00004504904,0.0001650182,0.00003297602,0.0001122946,0.002177725,0.1867107,0.7167385,0.005252945,0.08704744,0.0004925071],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9387838,0.0003423578,0.05764724,0.00005891021,0.0002512037,0.0001124037,0.000008743147,0.000525792,0.002269507],"genre_scores_gemma":[0.9441329,0.00002701289,0.05566758,0.00004239212,0.00003078928,0.0000125563,0.000006390361,0.00003178861,0.00004861641],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8094536,"threshold_uncertainty_score":0.7405331,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04954222911415612,"score_gpt":0.2618615295983527,"score_spread":0.2123193004841966,"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."}}