{"id":"W3204085390","doi":"10.1109/visap52981.2021.00011","title":"Deep Connection: Making Virtual Reality Artworks with Medical Scan Data","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Virtual Reality Applications and Impacts","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Killam Trusts; University of Alberta; Scan|Design Fonden v. Inger og Jens Bruun; Interface","keywords":"Virtual reality; Computer science; Connection (principal bundle); Embodied cognition; Human–computer interaction; Artificial intelligence; Computer graphics (images); Engineering","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"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":[0.004195529,0.0007746675,0.0002875772,0.001421049,0.001213357,0.006455484,0.001911065,0.001620251,0.0163237],"category_scores_gemma":[0.01757585,0.0007243124,0.0009441233,0.000899725,0.004567835,0.005165731,0.0140166,0.001721776,0.00254416],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004960747,"about_ca_system_score_gemma":0.0005762423,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003797144,"about_ca_topic_score_gemma":0.0006476414,"domain_scores_codex":[0.9957849,0.002353146,0.0001229431,0.0004740666,0.0009682259,0.0002967108],"domain_scores_gemma":[0.9936604,0.003547806,0.0002803732,0.001699357,0.0003213606,0.000490703],"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":[0.0009520544,0.0003939667,0.00531241,0.001047575,0.0002110174,0.002916937,0.05087043,0.02128232,0.04242903,0.2271507,0.05325854,0.594175],"study_design_scores_gemma":[0.0001855363,0.0006781085,0.005941934,0.0008653667,0.0001448324,0.0065308,0.01608538,0.0363577,0.02511899,0.2094029,0.6983532,0.0003353223],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"other","genre_scores_codex":[0.0748289,0.001080388,0.8265359,0.004565974,0.0009124749,0.000476517,0.0004672513,0.003347867,0.08778468],"genre_scores_gemma":[0.6047009,0.00134669,0.3663361,0.001496631,0.0004773703,0.0005399841,0.0006186507,0.001593421,0.02289028],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.0163237,"threshold_uncertainty_score":0.05460829,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08373519894998,"score_gpt":0.3445697152464911,"score_spread":0.2608345162965111,"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."}}