{"id":"W4200244887","doi":"10.31223/x5n33g","title":"Using satellites to uncover large methane emissions from landfills","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Atmospheric and Environmental Gas Dynamics","field":"Environmental Science","cited_by":22,"is_retracted":false,"has_abstract":true,"ca_institutions":"GHGSat (Canada)","funders":"","keywords":"Methane; Environmental science; Methane emissions; Emission inventory; Greenhouse gas; Leverage (statistics); Atmospheric methane; Satellite; Work (physics); Remote sensing; Meteorology; Air quality index; Geography; Engineering; Computer science; Geology","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":[0.0001280669,0.0003214905,0.0001791161,0.0009568873,0.0002052218,0.0003214326,0.0001496762,0.0002584794,0.0004616499],"category_scores_gemma":[0.0002191,0.0001369215,0.0002495502,0.001047331,0.000245066,0.0002780569,0.0003937719,0.0001912771,0.0000914367],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002091132,"about_ca_system_score_gemma":0.0002013464,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005754389,"about_ca_topic_score_gemma":0.01433031,"domain_scores_codex":[0.9999244,0.00001032736,0.000002195481,0.00001907897,0.00002353814,0.00002050632],"domain_scores_gemma":[0.9998953,0.00002236331,0.00003378802,0.00001778619,0.0000144415,0.00001631638],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003353425,0.0001292105,0.7075029,0.0001512072,0.0004197247,0.0009153361,0.0009505925,0.02838857,0.2019448,0.001186133,0.001558507,0.05651757],"study_design_scores_gemma":[0.00002647044,0.0001528643,0.8581833,0.00003510013,0.0001752026,0.0004974981,0.001532711,0.08353713,0.0493185,0.001603954,0.004896943,0.00004026591],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9961944,0.0001183621,0.001914662,0.00004454024,0.000005188244,0.000007009519,0.0005145606,0.0001502471,0.001051046],"genre_scores_gemma":[0.9935458,0.0001003326,0.005431719,0.00001674868,0.000006546247,0.000005933612,0.000670035,0.00001671707,0.0002061226],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005754389,"threshold_uncertainty_score":0.01144177,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02099566157974807,"score_gpt":0.2621784454406159,"score_spread":0.2411827838608678,"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."}}