{"id":"W7148709056","doi":"10.5281/zenodo.19397057","title":"Personalised, Context-Aware XR Training Applications Driven by Large Language Models","year":2024,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Augmented Reality Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Alpha Technologies (Canada)","funders":"","keywords":"Training (meteorology); Personalization; Generative grammar; Productivity; Generative model; Language model","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":[0.0005739477,0.0007023806,0.0004145109,0.0003289162,0.0002328284,0.001070224,0.0009327395,0.0008704786,0.004656984],"category_scores_gemma":[0.002068442,0.0003677108,0.0006105853,0.0002138802,0.0003093639,0.0009396242,0.001937699,0.0009109179,0.001928141],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000233306,"about_ca_system_score_gemma":0.0002831287,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005883469,"about_ca_topic_score_gemma":0.0008885763,"domain_scores_codex":[0.9996104,0.0001354158,0.00002736527,0.00009149602,0.0001031362,0.00003218438],"domain_scores_gemma":[0.9993386,0.000367326,0.0000464316,0.0001316956,0.00006818082,0.00004782273],"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":[0.001361206,0.001006504,0.004908248,0.001161304,0.000192166,0.002027773,0.005329662,0.1491306,0.2398403,0.01355588,0.02106213,0.5604242],"study_design_scores_gemma":[0.0001695719,0.0005120808,0.003725689,0.000113951,0.0001020772,0.001116019,0.0008083169,0.8508723,0.07420725,0.01247153,0.05575435,0.0001468802],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08271506,0.0003729576,0.8793511,0.0002792129,0.00008211106,0.0002736555,0.0004443245,0.02894828,0.007533264],"genre_scores_gemma":[0.6472998,0.0004955416,0.335996,0.000254867,0.0000511956,0.0004814745,0.001281043,0.001833932,0.01230632],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004656984,"threshold_uncertainty_score":0.0155791,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04094782736984303,"score_gpt":0.2695262316603087,"score_spread":0.2285784042904657,"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."}}