{"id":"W4230449234","doi":"10.22215/etd/2014-10103","title":"Proof-of-Concept Inverse Micro-Scale Dispersion Modelling for Fugitive Emissions Quantification in Industrial Facilities","year":2014,"lang":"en","type":"dissertation","venue":"","topic":"Wind and Air Flow Studies","field":"Environmental Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Inverse; Atmospheric dispersion modeling; Regularization (linguistics); Inverse problem; Dispersion (optics); Environmental science; Function (biology); Algorithm; Mathematics; Computer science; Air pollution; Physics; Optics; Chemistry; Mathematical analysis; Geometry; Artificial intelligence","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001660955,0.0002088438,0.0003096252,0.00007118363,0.0001433812,0.00001477067,0.0001567476,0.0002736879,0.0002763192],"category_scores_gemma":[0.00005551091,0.0001770208,0.0001198883,0.0001348865,0.0001129068,0.0001339567,0.00003732729,0.0001760898,0.0000166072],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009571296,"about_ca_system_score_gemma":0.00002755175,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007835091,"about_ca_topic_score_gemma":0.001431452,"domain_scores_codex":[0.9987506,0.00003745353,0.0003886106,0.0003999084,0.0002055927,0.0002178612],"domain_scores_gemma":[0.9994993,0.00009563744,0.0001781372,0.0001530743,0.00002302552,0.0000507842],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.002522564,0.00214302,0.02819181,0.001641246,0.0002463124,0.000002997075,0.1643827,0.2693921,0.09910695,0.001090412,0.1101647,0.3211153],"study_design_scores_gemma":[0.003155862,0.0004678743,0.001778361,0.001314468,0.0001603324,5.716828e-7,0.09576061,0.0903239,0.7578926,0.002064997,0.04540901,0.001671424],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9794199,0.00006912947,0.007620296,0.0001325999,0.0005215253,0.001227595,0.000203203,0.00002761651,0.01077806],"genre_scores_gemma":[0.9357994,0.00002516761,0.002046733,0.00002188534,0.0001313907,0.0001760318,0.001398783,0.00002903583,0.06037164],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6587856,"threshold_uncertainty_score":0.7218699,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04250285438044327,"score_gpt":0.2574296344943071,"score_spread":0.2149267801138638,"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."}}