{"id":"W138710780","doi":"10.1023/a:1023998231392","title":"Monitoring and Dispersion Modelling of Emissions from the Fluidised Bed Combustion of Poultry Litter","year":2003,"lang":"en","type":"article","venue":"Environmental Monitoring and Assessment","topic":"Odor and Emission Control Technologies","field":"Chemical Engineering","cited_by":6,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"Ministry of Agriculture, Forestry and Fisheries; Canada Excellence Research Chairs, Government of Canada; U.S. Environmental Protection Agency","keywords":"Combustion; Environmental science; Air quality index; NOx; Atmospheric dispersion modeling; Pollutant; Line source; Air pollution; Air pollutants; Environmental engineering; Dispersion (optics); Ground level; Particulates; Environmental chemistry; Waste management; Meteorology; Chemistry; Engineering; Physics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009564474,0.000128422,0.0001709829,0.00002204223,0.0001026368,0.00001121028,0.00007761818,0.00008354402,0.00001351245],"category_scores_gemma":[0.00001875018,0.00009356386,0.00004122682,0.00003399141,0.00006548703,0.00006713754,0.00007558557,0.000197734,3.425705e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004385849,"about_ca_system_score_gemma":0.000004438057,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000031167,"about_ca_topic_score_gemma":8.948259e-9,"domain_scores_codex":[0.9992626,0.00002581898,0.0002186134,0.0001799352,0.0001805507,0.0001325381],"domain_scores_gemma":[0.99953,0.0001652433,0.00006915982,0.0001749459,0.000003747945,0.00005690058],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000008061086,0.00004662944,0.2646291,0.00001487272,0.00002416887,5.187335e-7,0.0001437121,0.001453734,0.7285073,0.00003058498,0.000002808564,0.005138422],"study_design_scores_gemma":[0.0006892395,0.00004252496,0.0483994,0.000345782,0.00005890199,0.000001099373,0.004937113,0.005719584,0.9393449,0.0001932634,0.0001115449,0.0001566311],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9951443,0.001845247,0.00259204,0.00007637207,0.0001576338,0.00008024563,0.00001186532,0.0000290947,0.00006317893],"genre_scores_gemma":[0.9940522,0.001287956,0.004537329,0.000001268036,0.00006149297,0.000008176447,0.000003148689,0.00001237565,0.00003603583],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2162297,"threshold_uncertainty_score":0.3815423,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01968662048840416,"score_gpt":0.2434612253934055,"score_spread":0.2237746049050013,"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."}}