{"id":"W3016173369","doi":"10.48550/arxiv.2004.05235","title":"Using Conformity to Probe Interaction Challenges in XR Collaboration","year":2020,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Interactive and Immersive Displays","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Conformity; Virtuality (gaming); Virtualization; Computer science; Human–computer interaction; Psychology; Social psychology; Artificial intelligence","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003995019,0.0004434456,0.0004413808,0.00184041,0.001385223,0.003160162,0.0006257315,0.0009794431,0.002229726],"category_scores_gemma":[0.03447211,0.0002766971,0.0002979968,0.0009585365,0.001893751,0.00333476,0.004830315,0.001660067,0.0002868771],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006581612,"about_ca_system_score_gemma":0.0005001001,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00075603,"about_ca_topic_score_gemma":0.0005341957,"domain_scores_codex":[0.9928181,0.003206617,0.0003556947,0.0008548531,0.002246988,0.0005178275],"domain_scores_gemma":[0.9711344,0.01850424,0.003423898,0.003407156,0.002191868,0.001338517],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.003328963,0.001842944,0.1957133,0.0008452849,0.0002848074,0.001128899,0.07712574,0.04691721,0.3001018,0.1187625,0.002726736,0.2512218],"study_design_scores_gemma":[0.0002220261,0.004386423,0.2946613,0.0002902862,0.0001581097,0.002692917,0.04984063,0.332583,0.09535795,0.2033552,0.01587559,0.0005765036],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.870131,0.0001233536,0.1171284,0.0002308368,0.00003224483,0.0002008581,0.000132177,0.0003459245,0.01167528],"genre_scores_gemma":[0.9837735,0.00002736069,0.01537136,0.0000403757,0.000009644082,0.0001446437,0.00008248977,0.00004430443,0.0005064289],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003995019,"threshold_uncertainty_score":0.02112794,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.250752886018631,"score_gpt":0.2631531584650437,"score_spread":0.01240027244641267,"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."}}