{"id":"W2140795513","doi":"10.1177/0954411912474612","title":"Using additive manufacturing in accuracy evaluation of reconstructions from computed tomography","year":2013,"lang":"en","type":"article","venue":"Proceedings of the Institution of Mechanical Engineers Part H Journal of Engineering in Medicine","topic":"Anatomy and Medical Technology","field":"Engineering","cited_by":30,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"","keywords":"Segmentation; Reproducibility; Periosteum; Tomography; Computed tomography; Biomedical engineering; Cadaver; Artificial intelligence; Industrial computed tomography; Computer science; Computer vision; Computed tomographic; Materials science; Medicine; Radiology; Mathematics; Anatomy","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":[0.0008420327,0.0001586382,0.0005453769,0.0006859513,0.00001262806,0.000002570548,0.0003051925,0.0001767207,0.00005069358],"category_scores_gemma":[0.001372949,0.0001247896,0.0001066585,0.0006422097,0.0001433647,0.0002800823,0.00003732571,0.0005663222,2.029026e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001325891,"about_ca_system_score_gemma":0.00004567633,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004282054,"about_ca_topic_score_gemma":0.000001912195,"domain_scores_codex":[0.9981243,0.00001118954,0.001067917,0.000101554,0.000515616,0.0001794386],"domain_scores_gemma":[0.9989417,0.0001664533,0.00035791,0.00008570276,0.000374061,0.00007416579],"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":[0.00004640269,0.0001313926,0.0006288099,0.000503849,0.0003406233,0.000002954469,0.0005224383,0.8301172,0.1375046,0.008507396,0.0002688162,0.02142555],"study_design_scores_gemma":[0.002687604,0.0001467085,0.003812914,0.004729254,0.0001734441,0.00005416595,0.0005258326,0.6516075,0.3331585,0.002646257,0.0002764736,0.0001813033],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9874547,0.0007435424,0.01046545,0.00009004249,0.0008244872,0.0002634813,0.00000472615,0.00002710895,0.0001264357],"genre_scores_gemma":[0.993826,0.0002072454,0.005837759,0.000005519248,0.0001022051,0.000007443534,0.000001229202,0.00001223208,3.726621e-7],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1956539,"threshold_uncertainty_score":0.5088774,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02574987184636699,"score_gpt":0.2566851373652189,"score_spread":0.2309352655188519,"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."}}