{"id":"W4409743627","doi":"10.1002/cjce.25700","title":"Chemical process safety domain knowledge graph‐enhanced <scp>LLM</scp> for efficient emergency response decision support","year":2025,"lang":"en","type":"article","venue":"The Canadian Journal of Chemical Engineering","topic":"Risk and Safety Analysis","field":"Decision Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Science and Technology Planning Project of Guangdong Province; National Natural Science Foundation of China","keywords":"Computer science; Emergency response; Process (computing); Graph; Decision process; Domain (mathematical analysis); Process management; Engineering; Medical emergency; Medicine; Theoretical computer science; Operating system; Mathematics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000775565,0.0007138267,0.0003149336,0.002364987,0.0004774789,0.001209114,0.0009189122,0.000689673,0.004852306],"category_scores_gemma":[0.003528832,0.0002227026,0.001047054,0.001651497,0.0004393244,0.001714504,0.001643278,0.0007037622,0.000940943],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001013884,"about_ca_system_score_gemma":0.001671738,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008968486,"about_ca_topic_score_gemma":0.01042059,"domain_scores_codex":[0.9993161,0.0002544356,0.00004576074,0.0001297342,0.0002120035,0.00004197812],"domain_scores_gemma":[0.9986582,0.0006904995,0.000119132,0.0002594359,0.0002324001,0.00004027269],"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":[0.0001890361,0.0003856528,0.002734843,0.0008623839,0.0001507029,0.001167936,0.0006458907,0.3779522,0.0240901,0.06107806,0.02031849,0.5104247],"study_design_scores_gemma":[0.0000245205,0.00006201294,0.0007069741,0.00008349361,0.00005600409,0.000187892,0.0002726881,0.9183941,0.01373155,0.04158276,0.02486872,0.0000293899],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02178955,0.0001616817,0.9619027,0.0009976334,0.00004949694,0.0004017504,0.001618247,0.004717402,0.008361578],"genre_scores_gemma":[0.322171,0.000242046,0.6698522,0.0002878311,0.00002429632,0.0002789567,0.003750116,0.0002270614,0.003166412],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008968486,"threshold_uncertainty_score":0.01783252,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01651386059589308,"score_gpt":0.3068938228672535,"score_spread":0.2903799622713604,"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."}}