{"id":"W2557276749","doi":"10.1145/3009939.3009953","title":"Personalized Views for Immersive Analytics","year":2016,"lang":"en","type":"article","venue":"","topic":"Interactive and Immersive Displays","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ontario Tech University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Personalization; Visual analytics; Analytics; Computer science; Human–computer interaction; Cultural analytics; Context (archaeology); Eye tracking; Software analytics; Gaze; Data science; Semantic analytics; Visualization; World Wide Web; Artificial intelligence; The Internet; Software","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0008214595,0.0009683399,0.000428981,0.0004044285,0.0004453067,0.002711645,0.001060268,0.0009569742,0.01042397],"category_scores_gemma":[0.00431809,0.000482951,0.0006379055,0.0002796871,0.000805535,0.003696567,0.004456361,0.001794621,0.001659537],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002620669,"about_ca_system_score_gemma":0.0002263031,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005034886,"about_ca_topic_score_gemma":0.0008960437,"domain_scores_codex":[0.9990628,0.00030571,0.00004567652,0.0001920883,0.0002989538,0.00009471789],"domain_scores_gemma":[0.9974422,0.001187417,0.0001116245,0.0008372366,0.000224889,0.0001965578],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0009816973,0.000281982,0.002834476,0.001406154,0.000225537,0.00093299,0.01139577,0.01556815,0.2239933,0.1395901,0.02488719,0.5779027],"study_design_scores_gemma":[0.0002432485,0.001271904,0.00731243,0.0006882388,0.000396951,0.002690701,0.004294554,0.1400852,0.1139744,0.2026669,0.526008,0.0003676502],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02321672,0.001335313,0.9571522,0.0006252907,0.0002121405,0.0001347462,0.0002917608,0.005551007,0.01148076],"genre_scores_gemma":[0.495738,0.002035018,0.4866515,0.000911851,0.0004218835,0.0004508885,0.000808842,0.001609562,0.01137247],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01042397,"threshold_uncertainty_score":0.0348717,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04601215103819925,"score_gpt":0.3089245244853225,"score_spread":0.2629123734471233,"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."}}