{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009314177,0.00008622866,0.0001122902,0.00005498937,0.00006233773,0.00002909518,0.0003922532,0.00002527592,0.000233846],"category_scores_gemma":[0.00005742877,0.00004900631,0.0001466025,0.00009423137,0.00003282178,0.0004296218,0.00006732198,0.00002184518,0.0002908499],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000403277,"about_ca_system_score_gemma":0.00003154414,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004162844,"about_ca_topic_score_gemma":0.000002167249,"domain_scores_codex":[0.9993331,0.00001902426,0.0001092505,0.0002353957,0.00009415999,0.0002090583],"domain_scores_gemma":[0.9992881,0.0001938026,0.00005080248,0.0002295624,0.0001839674,0.00005381021],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00003373676,0.00004153174,0.0001449638,0.000005660365,0.00006734935,0.000002914313,0.0005262578,2.924388e-7,0.3561773,0.572305,0.06492777,0.005767265],"study_design_scores_gemma":[0.002234752,0.0003510412,0.001402952,0.00004494177,0.00002799738,0.00001312714,0.0004717266,0.003873911,0.4962629,0.007281276,0.4875888,0.0004465538],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0006387254,0.0000317723,0.9777251,0.00381619,0.0002393245,0.0001433391,0.000006734627,0.000006744707,0.01739209],"genre_scores_gemma":[0.9103317,0.00004353001,0.02640819,0.006536134,0.0001546089,0.00005868844,0.000003972948,0.00001557319,0.05644766],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9513169,"threshold_uncertainty_score":0.3738383,"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."}}