{"id":"W2979738756","doi":"10.24132/csrn.2019.2902.2.9","title":"Immersive Analytics Sensemaking on Different Platforms","year":2019,"lang":"en","type":"article","venue":"Computer Science Research Notes","topic":"Virtual Reality Applications and Impacts","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Sensemaking; Computer science; Analytics; Visual analytics; Human–computer interaction; Data science; Visualization; 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":[0.0009609318,0.000754493,0.00040336,0.0009047273,0.0004008625,0.002381247,0.0006640472,0.0005574437,0.003310934],"category_scores_gemma":[0.006126789,0.0003533827,0.0005313855,0.0003868474,0.0006922517,0.002180303,0.002814891,0.0004684875,0.0003702126],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001672356,"about_ca_system_score_gemma":0.0002019872,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004198392,"about_ca_topic_score_gemma":0.0006929672,"domain_scores_codex":[0.9989482,0.0003431574,0.00005356047,0.0002595981,0.0002828111,0.0001127074],"domain_scores_gemma":[0.9960788,0.002572042,0.0004017515,0.0004319433,0.0002787419,0.0002368704],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.002803124,0.001206824,0.03685552,0.002121287,0.0004218874,0.001531626,0.07744177,0.00889798,0.5833182,0.004660852,0.001819611,0.2789213],"study_design_scores_gemma":[0.0005649345,0.01504378,0.4311388,0.001154357,0.001124921,0.003794698,0.1432595,0.07650049,0.2309291,0.02279117,0.07266703,0.00103118],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9650658,0.0002204034,0.03074928,0.00008173435,0.00003827963,0.000118156,0.0001131185,0.000243457,0.003369813],"genre_scores_gemma":[0.9606535,0.0002480604,0.03657828,0.00006908756,0.00003543151,0.0001767087,0.0001824085,0.00006914979,0.001987492],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003310934,"threshold_uncertainty_score":0.01107621,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1624218790834614,"score_gpt":0.4155565937707238,"score_spread":0.2531347146872625,"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."}}