{"id":"W4401285608","doi":"10.18260/1-2--47054","title":"Board 57: Work in Progress: Immersive Learning: Maximizing Computer Networks Education Based on 3D Interactive Animations","year":2024,"lang":"en","type":"article","venue":"","topic":"Augmented Reality Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Animation; Multimedia; Process (computing); Human–computer interaction; Computer graphics (images)","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002086196,0.000154698,0.0001270909,0.0003316589,0.000130999,0.0004058265,0.0004331337,0.00007329831,0.00003785311],"category_scores_gemma":[0.00001549472,0.0001465144,0.00006288694,0.001375856,0.00004894362,0.0004857317,0.0001443435,0.0004654353,0.0001426472],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002761398,"about_ca_system_score_gemma":0.00020131,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000371862,"about_ca_topic_score_gemma":0.00001308918,"domain_scores_codex":[0.9986502,0.0001312005,0.0002577406,0.0005128377,0.0001991023,0.000248901],"domain_scores_gemma":[0.999051,0.000336885,0.00007650039,0.0003773156,0.00008254882,0.00007574649],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00003278528,0.0007371334,0.001624872,0.00004694945,0.00005870537,0.00001259791,0.003933572,0.4945397,0.00002239082,0.08773878,0.01649836,0.3947541],"study_design_scores_gemma":[0.0001020163,0.00006366821,0.00375095,0.0002913339,0.000006847502,0.000002336395,0.0001977395,0.9873692,0.00003297756,0.0002391651,0.007787908,0.0001557932],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0006276823,0.0001157298,0.9843658,0.008498025,0.0004732115,0.0004811706,6.696733e-7,0.0002955413,0.005142211],"genre_scores_gemma":[0.8888378,0.000007964414,0.1094885,0.0008285785,0.0001389051,0.0002962623,0.00003733436,0.00001716897,0.0003475758],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8882101,"threshold_uncertainty_score":0.5974682,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009995777586351355,"score_gpt":0.2746939041051987,"score_spread":0.2646981265188473,"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."}}