{"id":"W2961492131","doi":"10.24908/iqurcp.13286","title":"Virtual Reality in 3D Slicer","year":2019,"lang":"en","type":"article","venue":"Inquiry Queen s Undergraduate Research Conference Proceedings","topic":"Medical Imaging and Analysis","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"","keywords":"Virtual reality; Visualization; Computer science; Stereoscopy; Variety (cybernetics); Human–computer interaction; Rendering (computer graphics); Traverse; Multimedia; Computer graphics (images); Artificial intelligence","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.002075559,0.0002481649,0.0004122578,0.0006463881,0.00008122191,0.0002814141,0.0005596906,0.0001728044,0.0002579245],"category_scores_gemma":[0.0003424389,0.0002331156,0.0000747226,0.001339538,0.0003438938,0.0005335332,0.0001745919,0.001290978,0.0007905705],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002596192,"about_ca_system_score_gemma":0.0001448511,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004733159,"about_ca_topic_score_gemma":0.00002150296,"domain_scores_codex":[0.9967483,0.00007336941,0.0004483674,0.0005418189,0.001188167,0.0009999607],"domain_scores_gemma":[0.9986707,0.0001924515,0.00003749368,0.0002645302,0.000503801,0.0003310386],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001902308,0.0007875732,0.2849434,0.003313045,0.0008054684,0.0001885923,0.02559097,0.004349578,0.0838879,0.2528937,0.1364729,0.2065767],"study_design_scores_gemma":[0.00310786,0.0004104888,0.01333684,0.001814098,0.00005190321,0.00002565531,0.01221898,0.8355268,0.007583096,0.07620779,0.04767496,0.002041579],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9275773,0.00009589232,0.003692514,0.01292903,0.0003511215,0.0005466068,0.000005805309,0.0005660816,0.05423561],"genre_scores_gemma":[0.9969109,0.0005556618,0.0001742479,0.00006557355,0.0001659684,0.00006019146,0.00001085447,0.00004372752,0.002012824],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8311772,"threshold_uncertainty_score":0.9999874,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07441468012254744,"score_gpt":0.3528253817243939,"score_spread":0.2784107016018465,"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."}}