{"id":"W3201716672","doi":"","title":"AIive: Interactive Visualization and Sonification of Neural Network in Virtual Reality","year":2021,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Data Visualization and Analytics","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Sonification; Visualization; Computer science; Human–computer interaction; Virtual reality; Representation (politics); Artificial neural network; Range (aeronautics); Hyperparameter; Artificial intelligence; Multimedia; Engineering","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":[0.0002553515,0.0001516124,0.0002515216,0.0001776517,0.00004809748,0.0001101615,0.0005165524,0.0001396173,0.00001001661],"category_scores_gemma":[0.00006606695,0.0001889723,0.00005816878,0.0007914901,0.00007235855,0.0005656601,0.001077428,0.0002173119,0.000001568153],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008949999,"about_ca_system_score_gemma":0.0001154496,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001607855,"about_ca_topic_score_gemma":0.0001664908,"domain_scores_codex":[0.9986255,0.0002671292,0.0002504568,0.0006317139,0.00007615625,0.000149027],"domain_scores_gemma":[0.9987789,0.00008672628,0.000330639,0.0005357458,0.0002029954,0.00006504319],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002191009,0.0001349038,0.008264543,0.0000661612,0.0000374834,0.00004049395,0.001007789,0.4849323,0.00002406957,0.5043164,0.0001286589,0.001025173],"study_design_scores_gemma":[0.0002352655,0.00002755157,0.007649591,0.0001069672,0.00002095783,0.000001200264,0.0003448927,0.9872988,0.00007437902,0.004024561,0.00004773288,0.0001681418],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.214904,0.00002679082,0.7844477,0.00004606691,0.0001951287,0.0001125103,0.00001179768,0.00004079418,0.0002152411],"genre_scores_gemma":[0.9991107,0.0002495247,0.000242372,0.00006548588,0.00002818167,3.385466e-7,0.0001695823,0.00000681386,0.0001270255],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7842066,"threshold_uncertainty_score":0.7706065,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07143467700214003,"score_gpt":0.2516807783162679,"score_spread":0.1802461013141279,"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."}}