{"id":"W2561057401","doi":"10.5210/ojphi.v8i3.7100","title":"Beyond simple charts: Design of visualizations for big health data","year":2016,"lang":"en","type":"article","venue":"Online Journal of Public Health Informatics","topic":"Data Visualization and Analytics","field":"Computer Science","cited_by":48,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"","keywords":"Big data; Computer science; Data science; Sensemaking; Variety (cybernetics); Data visualization; Visualization; Population; Human–computer interaction; Data mining; Artificial intelligence; Medicine","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":[0.01407313,0.002169165,0.0009713901,0.003207669,0.00159867,0.00894792,0.003688664,0.001627018,0.006106157],"category_scores_gemma":[0.04012727,0.001515357,0.002070715,0.002248927,0.002502237,0.007188212,0.005016933,0.002869234,0.001259633],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001187053,"about_ca_system_score_gemma":0.002885654,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002504277,"about_ca_topic_score_gemma":0.003081485,"domain_scores_codex":[0.9931069,0.003773692,0.0006476415,0.0007733217,0.001318985,0.0003794465],"domain_scores_gemma":[0.9767712,0.01285327,0.001379122,0.003023671,0.004299989,0.001672678],"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":[0.001229153,0.0005266811,0.01122318,0.003712713,0.0004444236,0.002501723,0.03587823,0.08602326,0.03165703,0.3289671,0.05235347,0.4454831],"study_design_scores_gemma":[0.0005052663,0.0005636314,0.003137265,0.001517913,0.0003038027,0.0009627605,0.005692182,0.3898537,0.02794526,0.259985,0.3090621,0.0004710017],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.005622166,0.0002856499,0.9837363,0.0008670416,0.0001104189,0.0005097126,0.000378882,0.006237772,0.002251995],"genre_scores_gemma":[0.0448592,0.0003027685,0.9513615,0.000138266,0.00004127678,0.0005593866,0.0006119262,0.0009766059,0.001149034],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01407313,"threshold_uncertainty_score":0.07442671,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2713751422340808,"score_gpt":0.4382312369354769,"score_spread":0.1668560947013961,"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."}}