{"id":"W4396222774","doi":"10.9734/jamcs/2024/v39i51894","title":"Combining Numeric Method and Visualization Method Together to Analyze Big Data and the Prediction of the Rate of Accidental Death in China’s Coal Mining Industry","year":2024,"lang":"en","type":"article","venue":"Journal of Advances in Mathematics and Computer Science","topic":"Occupational Health and Safety Research","field":"Health Professions","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Accidental; Visualization; Big data; China; Coal mining; Data mining; Data science; Computer science; Mining industry; Coal; Data visualization; Mining engineering; Engineering; History; Waste management; Archaeology","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":[0.001374834,0.0006416166,0.0004015988,0.003188801,0.0003793661,0.001210833,0.0004777949,0.000472017,0.001345409],"category_scores_gemma":[0.004553191,0.0002076616,0.0007846162,0.001961727,0.0002683551,0.001564336,0.0006651579,0.0005010492,0.0002762785],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004021439,"about_ca_system_score_gemma":0.001026951,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006992894,"about_ca_topic_score_gemma":0.006804163,"domain_scores_codex":[0.9990101,0.0002802543,0.0001198846,0.0001970413,0.0003269121,0.00006572782],"domain_scores_gemma":[0.9981716,0.0008698322,0.0002117583,0.0001129992,0.0005300437,0.000103753],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003575223,0.0002913951,0.2726544,0.0007857947,0.0004125223,0.0007150473,0.001326437,0.1097017,0.008932446,0.01165227,0.01776231,0.575408],"study_design_scores_gemma":[0.00003936459,0.00008885197,0.06480402,0.0000775476,0.0001187357,0.0002437765,0.000895083,0.9081465,0.003805637,0.01525869,0.006439006,0.00008277145],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2970025,0.001777778,0.6867895,0.002257474,0.0004909767,0.0001541748,0.00239496,0.004191354,0.004941213],"genre_scores_gemma":[0.8578134,0.0008426653,0.1370887,0.000140919,0.0001596404,0.0001544948,0.001748008,0.00009584294,0.001956357],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006992894,"threshold_uncertainty_score":0.01390439,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09541462728043663,"score_gpt":0.5056664384175725,"score_spread":0.4102518111371359,"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."}}