{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001701885,0.0007510859,0.0005227647,0.0005618862,0.0003643352,0.001843287,0.001143583,0.001001379,0.01048078],"category_scores_gemma":[0.003928346,0.0006781488,0.001133446,0.0004421466,0.001170417,0.001267572,0.002646625,0.001048608,0.001902814],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00044442,"about_ca_system_score_gemma":0.0005748728,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00214,"about_ca_topic_score_gemma":0.001974505,"domain_scores_codex":[0.9981995,0.0009074032,0.00007606903,0.0001641459,0.000553533,0.00009936365],"domain_scores_gemma":[0.9985029,0.0008337263,0.00007080039,0.0003118591,0.0002002936,0.00008045353],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001710504,0.0004574391,0.00303989,0.001892666,0.0001980414,0.002146737,0.005133084,0.09127119,0.1635447,0.1854448,0.03497107,0.5101898],"study_design_scores_gemma":[0.0004860837,0.001975108,0.009142737,0.0009276291,0.0002406854,0.007272606,0.0009337766,0.24225,0.1000893,0.06371716,0.5721748,0.0007900737],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02376339,0.00138758,0.9393162,0.0005224225,0.0003495005,0.000517246,0.000606237,0.003173823,0.03036359],"genre_scores_gemma":[0.2338755,0.001774195,0.7499091,0.0006888298,0.0001117633,0.0008111311,0.0009378087,0.0009992284,0.01089241],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01048078,"threshold_uncertainty_score":0.03506172,"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."}}