{"id":"W4386223751","doi":"10.3390/atmos14091352","title":"Measurement of Long-Term CH4 Emissions and Emission Factors from Beef Feedlots in Australia","year":2023,"lang":"en","type":"article","venue":"Atmosphere","topic":"Atmospheric and Environmental Gas Dynamics","field":"Environmental Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta; Agriculture and Agri-Food Canada","funders":"","keywords":"Greenhouse gas; Feedlot; Environmental science; Methane; Emission inventory; Climate change; Atmospheric sciences; Animal science; Meteorology; Geography; Air quality index; Chemistry; Ecology","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":[0.0004717692,0.0002634271,0.0002318221,0.0005162742,0.0006390224,0.0005366376,0.0003324512,0.000276243,0.0002814367],"category_scores_gemma":[0.0004318949,0.0002378978,0.0002175685,0.0008478238,0.0002565489,0.0003590387,0.000332396,0.0002927969,0.000107563],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001547434,"about_ca_system_score_gemma":0.0006352582,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0959646,"about_ca_topic_score_gemma":0.17017,"domain_scores_codex":[0.9996394,0.00003986367,0.00001389241,0.00009963907,0.0001662225,0.00004089932],"domain_scores_gemma":[0.9997528,0.0000279176,0.0000466726,0.00001468791,0.0001322445,0.00002560579],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0005570429,0.0003205563,0.4829786,0.0001829505,0.0001029422,0.0003737031,0.00254828,0.00426613,0.468151,0.000179706,0.0002862802,0.04005278],"study_design_scores_gemma":[0.000006313525,0.0002506731,0.9731624,0.00001169521,0.00002208678,0.00008659413,0.0003854636,0.003760213,0.02123537,0.00004786353,0.001019518,0.00001177842],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.997853,0.00006933169,0.001208014,0.00001207893,0.000001636399,0.00001220476,0.0001546106,0.00001055116,0.0006785082],"genre_scores_gemma":[0.9953415,0.0001414779,0.002956886,0.00002126851,0.000002725661,0.00001997188,0.00044254,0.000007883607,0.001065757],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0959646,"threshold_uncertainty_score":0.1908121,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02419454601414837,"score_gpt":0.244897339315743,"score_spread":0.2207027933015946,"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."}}