{"id":"W4400985184","doi":"10.1145/3641520.3665305","title":"AI in mixed reality - Copilot on HoloLens: Spatial computing with large language models","year":2024,"lang":"en","type":"article","venue":"","topic":"3D Modeling in Geospatial Applications","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Microsoft (Canada)","funders":"","keywords":"Mixed reality; Computer science; Augmented reality; Human–computer interaction; 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":[0.001352618,0.0006988822,0.0006397137,0.0007488106,0.001714822,0.004306344,0.001604784,0.001335882,0.01077986],"category_scores_gemma":[0.003761223,0.0005303018,0.001093909,0.0007238639,0.00392056,0.006727117,0.004678349,0.002279758,0.002053516],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001558725,"about_ca_system_score_gemma":0.0007988704,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006825293,"about_ca_topic_score_gemma":0.009345369,"domain_scores_codex":[0.9992203,0.0003356855,0.00003468103,0.0001557876,0.0001958405,0.00005782005],"domain_scores_gemma":[0.9987774,0.0007334405,0.00002787139,0.0002174491,0.0001360292,0.0001077857],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0003500007,0.00009722228,0.0005961622,0.0003280648,0.00007610301,0.0005899589,0.003441405,0.03559886,0.008657044,0.7033716,0.02840672,0.2184868],"study_design_scores_gemma":[0.00006245675,0.00006621044,0.0003803559,0.0001868757,0.0000318861,0.0003484855,0.0008904403,0.3712893,0.01383156,0.4442464,0.1685806,0.00008541972],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"other","genre_scores_codex":[0.01027599,0.001197401,0.9576239,0.002504867,0.0002042683,0.00009109379,0.0001977849,0.00336676,0.02453794],"genre_scores_gemma":[0.2362901,0.002044511,0.7278879,0.0009259409,0.0001449993,0.0003057332,0.0007159085,0.001468737,0.0302161],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.01077986,"threshold_uncertainty_score":0.03606224,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01659931771363149,"score_gpt":0.2627393488444116,"score_spread":0.2461400311307801,"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."}}