{"id":"W4399463049","doi":"10.1007/s41810-024-00233-9","title":"Source Apportionment of Carbonaceous Aerosols during PM2.5 Pollution Episodes in Xi’an, Northwestern China: Estimation of the Potential of Carbon Emission Reduction by Rural Household Energy Substitution","year":2024,"lang":"en","type":"article","venue":"Aerosol Science and Engineering","topic":"Air Quality and Health Impacts","field":"Environmental Science","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"IONICS Mass Spectrometry (Canada)","funders":"","keywords":"Apportionment; China; Substitution (logic); Environmental science; Pollution; Carbon fibers; Estimation; Reduction (mathematics); Air pollution; Atmospheric sciences; Meteorology; Environmental engineering; Environmental chemistry; Geography; Chemistry; Economics; Mathematics; Political science","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002146023,0.0003426443,0.0002002348,0.0007840011,0.0003840884,0.0003667994,0.000408621,0.0003774977,0.000503576],"category_scores_gemma":[0.0002109182,0.0002129521,0.0003294541,0.0006210523,0.0001547058,0.0002235728,0.0003327879,0.0001400194,0.00007646102],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001016458,"about_ca_system_score_gemma":0.0005695128,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.09895428,"about_ca_topic_score_gemma":0.1174826,"domain_scores_codex":[0.9999015,0.00001090655,0.000008625539,0.00003073418,0.00002124688,0.00002704149],"domain_scores_gemma":[0.9998301,0.00002599828,0.00004621366,0.00001375399,0.00004823017,0.00003555239],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001223049,0.00004276307,0.9892077,0.00002272989,0.00008483553,0.000225264,0.0002902716,0.001255757,0.005079786,0.00007599295,0.0001393589,0.003453083],"study_design_scores_gemma":[0.000001803914,0.000008914802,0.9979685,0.000001364065,0.00001131498,0.00002136799,0.000163126,0.001484724,0.0002389095,0.0000157136,0.00008177449,0.000002572522],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.999545,0.00002310314,0.00005710882,0.00001113301,0.000001461817,0.000003564284,0.0001903845,0.00000326909,0.000164853],"genre_scores_gemma":[0.9994136,0.00001602724,0.00005715117,0.00000470885,0.000002311697,0.000004417553,0.0003313125,9.402293e-7,0.0001695917],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.09895428,"threshold_uncertainty_score":0.1967566,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00762393202675638,"score_gpt":0.2146171087588575,"score_spread":0.2069931767321011,"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."}}