{"id":"W3026980295","doi":"10.1016/j.jbi.2020.103458","title":"Visual storytelling enhances knowledge dissemination in biomedical science","year":2020,"lang":"en","type":"article","venue":"Journal of Biomedical Informatics","topic":"Media Influence and Health","field":"Arts and Humanities","cited_by":35,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"LUNGevity Foundation; Bristol-Myers Squibb Canada; V Foundation for Cancer Research","keywords":"Storytelling; Computer science; Human–computer interaction; Visualization; Data science; World Wide Web; Multimedia; Artificial intelligence; Narrative; Art","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":["metaresearch","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.002326609,0.0005361077,0.0002601232,0.0009646325,0.0006246718,0.00354236,0.0007822575,0.001186298,0.02848986],"category_scores_gemma":[0.03551629,0.0002353563,0.0003182974,0.000522039,0.0005845994,0.003607209,0.002489163,0.001001666,0.001714289],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004503229,"about_ca_system_score_gemma":0.0004404215,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004437833,"about_ca_topic_score_gemma":0.0006363201,"domain_scores_codex":[0.9985668,0.000887316,0.0000552036,0.0001513738,0.0002445536,0.00009482718],"domain_scores_gemma":[0.9517277,0.04375068,0.00170494,0.001037095,0.0008916878,0.0008880314],"domain_codex":null,"domain_gemma":"reporting","domain_candidate":"reporting","domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.007633905,0.004675043,0.03113478,0.005460154,0.0003780598,0.001414825,0.1195874,0.006894081,0.07442978,0.02278275,0.04068538,0.6849238],"study_design_scores_gemma":[0.004066007,0.01392897,0.2066148,0.004097815,0.002974147,0.002876971,0.1146601,0.09060463,0.08750702,0.1147588,0.3572161,0.0006947717],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8861153,0.00114878,0.02701756,0.003331147,0.0003474708,0.0004516065,0.0006202566,0.00177231,0.07919562],"genre_scores_gemma":[0.972473,0.0005711883,0.01713747,0.0006146906,0.0001963022,0.0002653348,0.0003922985,0.000261357,0.008088232],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9976734,"threshold_uncertainty_score":0.09530807,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0422049048795932,"score_gpt":0.3465043087079537,"score_spread":0.3042994038283605,"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."}}