{"id":"W2989931748","doi":"10.1289/isesisee.2018.o03.03.02","title":"Applying a Kinetic Multi-Layer Model of Surface and Bulk Chemistry in Epithelial Lung Lining Fluid to Estimate Spatial Variations in the Production of Reactive Oxygen Species in Response to PM2.5 Iron and Copper","year":2018,"lang":"en","type":"article","venue":"ISEE Conference Abstracts","topic":"Air Quality and Health Impacts","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Health Canada; University of Toronto; McGill University","funders":"","keywords":"Environmental chemistry; Reactive oxygen species; Pollutant; Particulates; Chemistry; Environmental science; Air pollution; Spatial variability; Biochemistry","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001220369,0.0001085098,0.000179497,0.00005156946,0.00005223593,0.00001777646,0.0001096961,0.00007367077,0.00002342659],"category_scores_gemma":[0.0008788535,0.00009684367,0.000008143776,0.000175978,0.0001477388,0.0001644047,0.00008314969,0.0001448297,0.00000452274],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009530662,"about_ca_system_score_gemma":0.00007811186,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003025818,"about_ca_topic_score_gemma":0.003599462,"domain_scores_codex":[0.9988418,0.00009596603,0.0003756288,0.0002723543,0.0001911074,0.000223152],"domain_scores_gemma":[0.9994128,0.00018998,0.0001196821,0.0001669063,0.00002627754,0.00008429211],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.0005576461,0.00009839098,0.004567042,0.00004814609,0.000001250633,0.000001365175,0.02587488,0.2787075,0.6891671,0.00001138419,0.00001268652,0.0009526218],"study_design_scores_gemma":[0.0003763722,0.00009034912,0.9386786,0.0001919261,0.000004556356,0.000002112285,0.0008000125,0.03225379,0.02739661,0.00007386116,0.00002331371,0.0001084501],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9967494,0.00000752063,0.0005932049,0.001730851,0.00002378295,0.0006785291,0.000014563,0.000004546197,0.0001975762],"genre_scores_gemma":[0.9953845,0.000009622247,0.004384793,0.0001177966,0.0000159032,0.00003342709,0.000001774078,0.000006075603,0.00004612024],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9341116,"threshold_uncertainty_score":0.4574152,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06915565191345423,"score_gpt":0.3385101178716494,"score_spread":0.2693544659581952,"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."}}