{"id":"W6930691549","doi":"10.5281/zenodo.15366148","title":"Improved monitoring of methane emissions for the oil and gas sector with Sentinel-2 satellite observations","year":2025,"lang":"en","type":"other","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Carbohydrate Chemistry and Synthesis","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Government of Alberta; University of Alberta","funders":"","keywords":"Methane; Satellite; Methane emissions; Greenhouse gas; Context (archaeology); Python (programming language); Earth observation satellite; Carbon dioxide","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.001199607,0.0007841831,0.0003680615,0.001232258,0.0003391507,0.0005829264,0.001086845,0.0004515783,0.007445489],"category_scores_gemma":[0.001592702,0.0002285411,0.0006000787,0.001682948,0.0001633038,0.0008552526,0.0008470317,0.0005417663,0.004565836],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006320941,"about_ca_system_score_gemma":0.001662733,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03005795,"about_ca_topic_score_gemma":0.04878152,"domain_scores_codex":[0.9995928,0.00007680473,0.00001865003,0.00008295422,0.0001691827,0.00005946897],"domain_scores_gemma":[0.9995694,0.00005864228,0.00003574905,0.0001311378,0.0001556708,0.0000495306],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"observational","study_design_scores_codex":[0.001435333,0.0004777152,0.04431345,0.001349723,0.0004848158,0.0003152426,0.0001966151,0.09817113,0.02844241,0.006442679,0.6095018,0.208869],"study_design_scores_gemma":[0.0005460169,0.0002857875,0.07073417,0.0002243279,0.0001913026,0.0002667572,0.0002264565,0.5620473,0.05533445,0.009656365,0.3002463,0.0002408105],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.08486652,0.0006505935,0.1067862,0.001107815,0.0005507663,0.000381477,0.7039034,0.07365776,0.02809544],"genre_scores_gemma":[0.1570614,0.0003205398,0.1471204,0.0002322063,0.00009530396,0.0002755606,0.6854793,0.003176909,0.006238431],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.03005795,"threshold_uncertainty_score":0.05976593,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03986746846225155,"score_gpt":0.2415413100589859,"score_spread":0.2016738415967343,"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."}}