{"id":"W4291019416","doi":"10.1126/sciadv.abn9683","title":"Using satellites to uncover large methane emissions from landfills","year":2022,"lang":"en","type":"article","venue":"Science Advances","topic":"Atmospheric and Environmental Gas Dynamics","field":"Environmental Science","cited_by":183,"is_retracted":false,"has_abstract":true,"ca_institutions":"GHGSat (Canada)","funders":"","keywords":"Methane; Environmental science; Methane emissions; Atmospheric methane; Astrobiology; Environmental chemistry; Computer science; Remote sensing; Geology; Chemistry; Ecology; Biology","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":[0.0001566438,0.0003081188,0.000182095,0.001026251,0.000214055,0.0003322066,0.000167466,0.0002409432,0.0004336993],"category_scores_gemma":[0.0002564464,0.0001326956,0.0002651193,0.001112467,0.0002217462,0.0003067249,0.000405293,0.000189464,0.00007360959],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002907075,"about_ca_system_score_gemma":0.0002970687,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01017116,"about_ca_topic_score_gemma":0.02860274,"domain_scores_codex":[0.9999212,0.00001258547,0.000002852343,0.00001719205,0.00002415608,0.000021896],"domain_scores_gemma":[0.99989,0.00002265224,0.00003696118,0.000016565,0.00001861951,0.00001527551],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002802433,0.0001104675,0.8070163,0.0001115001,0.0003205104,0.0005862816,0.0006891786,0.02511612,0.1163186,0.0007868664,0.001092465,0.04757139],"study_design_scores_gemma":[0.00002586937,0.0001527017,0.8718925,0.00004023629,0.0001676278,0.0003335021,0.00169316,0.09222648,0.02879371,0.001177171,0.003457956,0.00003903585],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9969327,0.00009851318,0.001379031,0.00004944294,0.000004908117,0.000007152242,0.0004637631,0.00009833081,0.0009660437],"genre_scores_gemma":[0.9948949,0.00008315543,0.004303176,0.00001658828,0.000004753738,0.000005674534,0.0005240088,0.000008245634,0.0001594639],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01017116,"threshold_uncertainty_score":0.02022392,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01184513221740734,"score_gpt":0.2674741820972524,"score_spread":0.255629049879845,"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."}}