{"id":"W2948173255","doi":"10.1016/j.aeaoa.2019.100035","title":"Computationally efficient quantification of unknown fugitive emissions sources","year":2019,"lang":"en","type":"article","venue":"Atmospheric Environment X","topic":"Atmospheric and Environmental Gas Dynamics","field":"Environmental Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"Natural Resources Canada; Natural Sciences and Engineering Research Council of Canada; Petroleum Technology Alliance Canada","keywords":"Fugitive emissions; Bluff; Computation; Greenhouse gas; Environmental science; Scalar (mathematics); Work (physics); Computational fluid dynamics; Wind direction; Computer science; Meteorology; Wind speed; Mechanics; Engineering; Algorithm; Mathematics; Geology; Geometry; Physics; Mechanical engineering","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0004194374,0.0005341481,0.0005135337,0.0004512054,0.0003079514,0.001045815,0.0007550702,0.0006736882,0.001542269],"category_scores_gemma":[0.001220342,0.0003797547,0.0004642564,0.0003710673,0.000370693,0.000913861,0.0007613294,0.0007207519,0.00016379],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005062075,"about_ca_system_score_gemma":0.001168632,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009477621,"about_ca_topic_score_gemma":0.008402423,"domain_scores_codex":[0.999869,0.00001979186,0.000006008958,0.00002651271,0.00005170962,0.00002702952],"domain_scores_gemma":[0.9995438,0.000278223,0.00004770191,0.00003164347,0.00007436213,0.00002425722],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00008371684,0.00003962409,0.001995609,0.00005300262,0.00001778112,0.0001388574,0.00005432788,0.9729848,0.007124851,0.002242022,0.0003425616,0.0149229],"study_design_scores_gemma":[0.000006120135,0.000006296663,0.0002424986,0.000001924866,0.000001987794,0.00001014174,0.00001291046,0.9982067,0.00100958,0.0003605263,0.0001384897,0.000002730148],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4722314,0.0002392358,0.5173557,0.000285143,0.00004727345,0.00008806183,0.0005080015,0.001480345,0.007764821],"genre_scores_gemma":[0.8963513,0.0001001319,0.1005742,0.00004168086,0.00001976642,0.00005712865,0.0004662072,0.0001398392,0.002249741],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009477621,"threshold_uncertainty_score":0.0188449,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004938737590818524,"score_gpt":0.1921063607899288,"score_spread":0.1871676231991102,"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."}}