{"id":"W4280542963","doi":"10.1071/aj21080","title":"Strength in numbers: how the different satellite systems used to monitor methane emissions from space have different, yet complementary, capabilities to help the oil and gas industry meet its decarbonisation goals","year":2022,"lang":"en","type":"article","venue":"The APPEA Journal","topic":"Atmospheric and Environmental Gas Dynamics","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institute of Aging; GHGSat (Canada)","funders":"","keywords":"Confusion; Methane; Methane emissions; Petroleum industry; Satellite; Work (physics); Strengths and weaknesses; Fossil fuel; Analytics; Natural gas; Computer science; Environmental science; Systems engineering; Engineering; Data science; Aerospace engineering; Waste management; Mechanical engineering; Environmental engineering","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":[0.0206804,0.001265039,0.000759207,0.002559267,0.002132162,0.009109469,0.002293287,0.002038591,0.006605811],"category_scores_gemma":[0.07594085,0.0007658372,0.0006287004,0.00260735,0.005754315,0.0211907,0.009947655,0.003438393,0.003628675],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002322099,"about_ca_system_score_gemma":0.0026192,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005393209,"about_ca_topic_score_gemma":0.004827783,"domain_scores_codex":[0.9836279,0.006110082,0.001099436,0.001643583,0.006733678,0.0007854349],"domain_scores_gemma":[0.9484425,0.02011341,0.005430891,0.006545972,0.01736901,0.002098203],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0009326196,0.0001757901,0.1419948,0.002102334,0.0006087084,0.000609453,0.008672614,0.01786109,0.01115024,0.2285464,0.02766489,0.559681],"study_design_scores_gemma":[0.0001800819,0.001576128,0.0584566,0.002519489,0.0007127944,0.00297218,0.01837951,0.0399614,0.01461652,0.3725051,0.4875356,0.0005845404],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.3738106,0.01223238,0.1657644,0.09625569,0.004215722,0.0004057483,0.001128176,0.00122027,0.344967],"genre_scores_gemma":[0.9417746,0.003246164,0.03729637,0.003638247,0.000766736,0.0001022669,0.0003679785,0.0003012862,0.01250634],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0206804,"threshold_uncertainty_score":0.1093697,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02285167880681738,"score_gpt":0.2385177148701471,"score_spread":0.2156660360633297,"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."}}