{"id":"W3203764532","doi":"10.1177/14738716211045354","title":"Which emphasis technique to use? Perception of emphasis techniques with varying distractors, backgrounds, and visualization types","year":2021,"lang":"en","type":"article","venue":"Information Visualization","topic":"Data Visualization and Analytics","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"Natural Sciences and Engineering Research Council of Canada; Canada First Research Excellence Fund","keywords":"Emphasis (telecommunications); Computer science; Visualization; Perception; Predictability; Visual perception; Human–computer interaction; Data science; Artificial intelligence; Psychology","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":[0.003114327,0.000648796,0.0004014198,0.0008907808,0.0003890002,0.002458185,0.0005021218,0.00111012,0.002129648],"category_scores_gemma":[0.03350394,0.00040779,0.0004774643,0.0004274154,0.0005476818,0.002567389,0.001075295,0.0009715923,0.0004696317],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003714524,"about_ca_system_score_gemma":0.0003405225,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001032041,"about_ca_topic_score_gemma":0.001282004,"domain_scores_codex":[0.9983258,0.0006399049,0.0001414386,0.0003315575,0.0004128071,0.0001485587],"domain_scores_gemma":[0.9800126,0.01436151,0.001660686,0.00102536,0.001955549,0.0009843217],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.005597916,0.0004753832,0.09916867,0.004158697,0.0004545159,0.001021313,0.02361983,0.003197778,0.4954161,0.004347537,0.004974395,0.3575679],"study_design_scores_gemma":[0.0007314199,0.00820538,0.6460656,0.003407189,0.002307683,0.00525848,0.03870815,0.04752631,0.1605739,0.01607135,0.07010517,0.001039362],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9478627,0.002952501,0.03916058,0.0005752115,0.0001735748,0.0001535405,0.0001676771,0.0006693453,0.008284812],"genre_scores_gemma":[0.956525,0.001300962,0.0402473,0.0002321742,0.0000485815,0.0000852627,0.0001190934,0.0003042712,0.001137413],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003114327,"threshold_uncertainty_score":0.01647031,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01854502352333071,"score_gpt":0.3057490524211677,"score_spread":0.287204028897837,"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."}}