{"id":"W2754431019","doi":"10.3390/ijerph14091056","title":"Collaborative Visual Analytics: A Health Analytics Approach to Injury Prevention","year":2017,"lang":"en","type":"article","venue":"International Journal of Environmental Research and Public Health","topic":"Delphi Technique in Research","field":"Social Sciences","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia; BC Children's Hospital; Simon Fraser University","funders":"Canadian Institutes of Health Research","keywords":"Analytics; Visual analytics; Delphi method; Stakeholder; Computer science; Data analysis; Social media analytics; Data science; Stakeholder engagement; Knowledge management; Visualization; Data mining; Artificial intelligence; Social media; World Wide Web","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":[0.02063532,0.001323871,0.0006242921,0.006915146,0.002642352,0.008674259,0.003214345,0.001793312,0.005605083],"category_scores_gemma":[0.02999474,0.000556707,0.001364855,0.004002585,0.005830959,0.006419192,0.008875486,0.002189133,0.0007060277],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003027861,"about_ca_system_score_gemma":0.005220755,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003054691,"about_ca_topic_score_gemma":0.003009393,"domain_scores_codex":[0.9796401,0.01617212,0.0006780369,0.001208962,0.001808098,0.0004926928],"domain_scores_gemma":[0.9673041,0.02545312,0.001253127,0.002589942,0.002213039,0.001186763],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0005799473,0.0007456435,0.009517222,0.003619748,0.0003795835,0.0008232955,0.09657384,0.01824807,0.006912381,0.3215401,0.02276177,0.5182984],"study_design_scores_gemma":[0.0002438136,0.000619424,0.004601514,0.003007851,0.000240254,0.0009148681,0.05895923,0.09750375,0.007449933,0.6786397,0.147611,0.0002086377],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0260323,0.001426615,0.9281762,0.01215695,0.000273049,0.00132855,0.0004770031,0.001591561,0.02853778],"genre_scores_gemma":[0.3048589,0.001103102,0.6878035,0.0009530494,0.0002162298,0.001304506,0.0004800808,0.0002224953,0.00305812],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02063532,"threshold_uncertainty_score":0.1091313,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2132813063543361,"score_gpt":0.5625165874907857,"score_spread":0.3492352811364496,"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."}}