{"id":"W2993580107","doi":"10.3390/ijerph16244972","title":"Slip and Fall Incidents at Work: A Visual Analytics Analysis of the Research Domain","year":2019,"lang":"en","type":"article","venue":"International Journal of Environmental Research and Public Health","topic":"Occupational Health and Safety Research","field":"Health Professions","cited_by":38,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University","funders":"Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Slip (aerodynamics); Data science; Poison control; Visualization; Visual analytics; Cluster analysis; Computer science; Engineering; Forensic engineering; Data mining; Medicine; Artificial intelligence","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01743619,0.0001181086,0.0004190085,0.001682881,0.0006989608,0.00004608694,0.0007108424,0.0001440952,0.0008932544],"category_scores_gemma":[0.001077296,0.00008234241,0.000121202,0.00130658,0.0006165709,0.0002597339,0.001250611,0.001872704,0.00006161729],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001723732,"about_ca_system_score_gemma":0.001551979,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006429612,"about_ca_topic_score_gemma":0.0005277118,"domain_scores_codex":[0.9902538,0.003052979,0.001119998,0.0002817508,0.004298078,0.0009933687],"domain_scores_gemma":[0.9938932,0.003742636,0.0004943658,0.0002629097,0.0007269224,0.0008799687],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0006362194,0.000344866,0.9839205,0.00007305591,0.0003896905,0.000006666993,0.001622031,0.000006645908,0.0001384227,0.0008312794,0.001189323,0.0108413],"study_design_scores_gemma":[0.00106724,0.0005926348,0.975899,0.0001310764,0.0000080252,0.000009487081,0.003390301,0.0002441051,0.000003184165,0.000681143,0.01791625,0.00005758984],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9805244,0.0007971657,0.00002442396,0.01632007,0.0002930975,0.0006775243,0.000120225,0.000002482621,0.001240631],"genre_scores_gemma":[0.9942039,0.002970339,0.00009924192,0.0004830039,0.0001966095,0.00001543491,0.00003030445,0.00001424876,0.001986879],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01672693,"threshold_uncertainty_score":0.9780509,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1767151462057308,"score_gpt":0.5394999840586251,"score_spread":0.3627848378528943,"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."}}