{"id":"W2155733062","doi":"10.1145/1168149.1168162","title":"Heuristics for information visualization evaluation","year":2006,"lang":"en","type":"article","venue":"","topic":"Data Visualization and Analytics","field":"Computer Science","cited_by":136,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Heuristics; Computer science; Generalizability theory; Visualization; Heuristic; Categorization; Data visualization; Information visualization; Data science; Process (computing); Heuristic evaluation; Machine learning; Data mining; Artificial intelligence; Information retrieval; Human–computer interaction; Psychology","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":[0.0003702425,0.00005192302,0.00004733989,0.0001040519,0.00007767278,0.0002565021,0.0001802844,0.00002761597,0.00002053633],"category_scores_gemma":[0.000118215,0.00004878016,0.00002146586,0.0002875444,0.000007010531,0.001294593,0.00003124936,0.00001145808,0.00005180368],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003035593,"about_ca_system_score_gemma":0.00004992339,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001282179,"about_ca_topic_score_gemma":0.000006737564,"domain_scores_codex":[0.9993214,0.00002044908,0.0002137134,0.00008728129,0.0002677341,0.00008942066],"domain_scores_gemma":[0.9992592,0.00003033291,0.00008285536,0.0001688442,0.000438987,0.00001981754],"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":[6.16481e-7,0.00001761607,0.00008398821,0.00000708934,0.000001352527,1.609756e-8,0.00002629508,0.001225641,0.00001686922,0.9621171,0.02581,0.01069342],"study_design_scores_gemma":[0.0002680221,0.00001673816,0.0004196195,0.000002106613,0.000006157643,4.807189e-7,0.000008350265,0.8970736,0.0004962966,0.01462457,0.08701734,0.00006672304],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0001017257,0.000005613304,0.9890205,0.0001754145,0.0001560585,0.0002204025,0.000007786734,0.0001351986,0.0101773],"genre_scores_gemma":[0.8522017,0.000008323066,0.1411754,0.002110593,0.0002127941,0.00007146566,0.002812053,0.00001026232,0.001397375],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9474925,"threshold_uncertainty_score":0.2473456,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02442890999791555,"score_gpt":0.3316553438817261,"score_spread":0.3072264338838105,"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."}}