{"id":"W2757349124","doi":"10.3390/informatics4040033","title":"Health Literacy for the General Public: Making a Case for Non-Trivial Visualizations","year":2017,"lang":"en","type":"article","venue":"Informatics","topic":"Data Visualization and Analytics","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"","keywords":"Health literacy; Visualization; Computer science; Literacy; Public health; Data visualization; Data science; Human–computer interaction; Psychology; Health care; Medicine; Artificial intelligence; Pedagogy; Nursing","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.03421758,0.0006735554,0.0004898485,0.001653697,0.003838618,0.008045089,0.002294783,0.005291742,0.007674048],"category_scores_gemma":[0.1132145,0.00052935,0.0009286357,0.0007931964,0.006642784,0.01484871,0.008737484,0.005502407,0.0007903983],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002457244,"about_ca_system_score_gemma":0.004702825,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004223227,"about_ca_topic_score_gemma":0.00791207,"domain_scores_codex":[0.972937,0.02237931,0.000573254,0.0008329421,0.001786399,0.001491089],"domain_scores_gemma":[0.8540899,0.1207013,0.004079189,0.006181041,0.007186124,0.007762416],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.000825785,0.001859301,0.07264691,0.003088726,0.0001026137,0.004198681,0.216207,0.001205037,0.003864413,0.1150548,0.07874736,0.5021994],"study_design_scores_gemma":[0.0003780689,0.001882631,0.03820476,0.009351416,0.0002401868,0.004924829,0.2100219,0.005976128,0.007008453,0.1595072,0.5621876,0.0003168721],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.3371665,0.006110717,0.03112695,0.5697467,0.001034666,0.0003727021,0.0001619746,0.0007285207,0.05355138],"genre_scores_gemma":[0.9383133,0.003513405,0.04121365,0.01229323,0.0003223811,0.0005110728,0.0001090282,0.0001657053,0.003558215],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.03421758,"threshold_uncertainty_score":0.180962,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07951790359042882,"score_gpt":0.4369266240304251,"score_spread":0.3574087204399963,"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."}}