{"id":"W4306920820","doi":"10.3390/atmos13101722","title":"Spatially Resolved Source Apportionment of Industrial VOCs Using a Mobile Monitoring Platform","year":2022,"lang":"en","type":"article","venue":"Atmosphere","topic":"Atmospheric chemistry and aerosols","field":"Earth and Planetary Sciences","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ministry of the Environment, Conservation and Parks","funders":"","keywords":"Environmental science; Oil refinery; Petrochemical; Air quality index; Fugitive emissions; Volatile organic compound; Aerosol; Gasoline; Air pollution; Pollution; Apportionment; Petroleum; Environmental engineering; Refinery; Environmental chemistry; Waste management; Greenhouse gas; Meteorology; Chemistry","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009094919,0.00032488,0.0002544947,0.0007506658,0.0003608662,0.0003955286,0.000265112,0.0002186595,0.0006129444],"category_scores_gemma":[0.0001394765,0.0001428658,0.0002212043,0.0007279238,0.0001428331,0.0002039807,0.0003653752,0.0001787967,0.000161915],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005873647,"about_ca_system_score_gemma":0.0005409569,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.07451665,"about_ca_topic_score_gemma":0.178755,"domain_scores_codex":[0.9999028,0.000004794902,0.000002090531,0.00003825297,0.00003284104,0.00001911149],"domain_scores_gemma":[0.9999444,0.000006649104,0.00001287703,0.000006607926,0.00002245659,0.000006948425],"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":[0.0004155798,0.0001629272,0.2252508,0.0001357499,0.0001176098,0.0004894579,0.0007103203,0.01110349,0.6964616,0.0002578753,0.0006660434,0.06422854],"study_design_scores_gemma":[0.00003707698,0.00033995,0.7804101,0.00002504587,0.0001219584,0.0003202849,0.001003878,0.09077433,0.1217341,0.0002943667,0.004872569,0.00006631166],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9896156,0.0001224054,0.007929928,0.0000182188,0.000006311587,0.00003106894,0.0008739578,0.00009787755,0.001304679],"genre_scores_gemma":[0.9849866,0.0001340672,0.01337433,0.00001256061,0.000006678147,0.00002985959,0.0007162027,0.00001549751,0.00072409],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07451665,"threshold_uncertainty_score":0.1481658,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03735416431145729,"score_gpt":0.2287586377911972,"score_spread":0.1914044734797399,"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."}}