{"id":"W4391093312","doi":"10.1109/bigdata59044.2023.10386099","title":"Visual Insight Recommendation: From Ranking Insight Visualizations to Insight Types","year":2023,"lang":"en","type":"article","venue":"","topic":"Data Visualization and Analytics","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Kootenay Association for Science & Technology","funders":"","keywords":"Computer science; Visualization; Ranking (information retrieval); Categorical variable; Focus (optics); Variety (cybernetics); Recommender system; Visual analytics; Information retrieval; Rank (graph theory); Data visualization; Class (philosophy); Learning to rank; Human–computer interaction; Information visualization; Data science; Data mining; Machine learning; 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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.000234874,0.0002171254,0.0002377131,0.0005995315,0.0003355521,0.000621494,0.0007794971,0.00009090976,0.0009511299],"category_scores_gemma":[0.0001869124,0.0002007881,0.00006672506,0.003855347,0.00002565344,0.001102202,0.0006786444,0.0001048166,0.002813736],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005329531,"about_ca_system_score_gemma":0.000106431,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007807635,"about_ca_topic_score_gemma":0.0001220518,"domain_scores_codex":[0.9980807,0.0001284692,0.000463158,0.0006067797,0.0003899133,0.0003309936],"domain_scores_gemma":[0.9987868,0.0001641501,0.00009844144,0.0005104499,0.0002134507,0.0002267147],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00001745608,0.0004636848,0.002477988,0.00002966966,0.0002134657,0.00004883303,0.01415149,0.001483428,0.002788984,0.6884334,0.1711879,0.1187037],"study_design_scores_gemma":[0.0003761845,0.0000558166,0.001725298,0.00002890199,0.0000132622,0.000001462363,0.000087083,0.282006,0.002716819,0.00129243,0.7113349,0.0003618107],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002259432,0.00002881278,0.9650041,0.005174537,0.0009326336,0.0002155214,0.0000257693,0.001213207,0.02514603],"genre_scores_gemma":[0.8726519,0.0005454903,0.05329129,0.04230841,0.001272995,0.0001166119,0.005641024,0.0001666033,0.02400572],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9117128,"threshold_uncertainty_score":0.9999622,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04002837939470994,"score_gpt":0.3355294241055158,"score_spread":0.2955010447108058,"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."}}