{"id":"W4311376223","doi":"10.5194/amt-15-7155-2022","title":"Detecting and quantifying methane emissions from oil and gas production: algorithm development with ground-truth calibration based on Sentinel-2 satellite imagery","year":2022,"lang":"en","type":"article","venue":"Atmospheric measurement techniques","topic":"Atmospheric and Environmental Gas Dynamics","field":"Environmental Science","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"GHGSat (Canada)","funders":"California Air Resources Board","keywords":"Ground truth; Methane; Satellite; Plume; Algorithm; Environmental science; Calibration; Remote sensing; Threshold limit value; Pixel; Sensitivity (control systems); Accuracy and precision; Computer science; Meteorology; Mathematics; Geology; Statistics; Physics; Chemistry; Artificial intelligence","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":[0.003612423,0.0008135728,0.0004067937,0.0008864356,0.0003150186,0.0006445911,0.00107446,0.0007453092,0.0007221506],"category_scores_gemma":[0.004787434,0.0003860141,0.0006372025,0.0005549716,0.0003389097,0.0009603181,0.000578926,0.0007805932,0.0004444685],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006846233,"about_ca_system_score_gemma":0.001195919,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006450192,"about_ca_topic_score_gemma":0.006036588,"domain_scores_codex":[0.9991652,0.0001928503,0.0000616473,0.000219511,0.000300214,0.00006057324],"domain_scores_gemma":[0.9982573,0.0004983205,0.0001873999,0.0001930904,0.0008205133,0.00004330539],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0003806057,0.0005707665,0.0321844,0.0002785543,0.0003656998,0.000149246,0.0002780715,0.4068999,0.1242085,0.002306975,0.00383015,0.4285471],"study_design_scores_gemma":[0.00003652096,0.00007646844,0.004331198,0.00001219735,0.00002235618,0.00004626674,0.00003422069,0.9648201,0.02890078,0.000451564,0.001244796,0.00002361054],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1460986,0.00029914,0.8468993,0.0002023718,0.00008178781,0.0002499265,0.000241111,0.004752269,0.001175434],"genre_scores_gemma":[0.3016471,0.0001125577,0.6965954,0.0001189939,0.0000146554,0.0001673431,0.0007947455,0.0001766885,0.0003725897],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.006450192,"threshold_uncertainty_score":0.0191046,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02293953246284035,"score_gpt":0.2151255973615516,"score_spread":0.1921860648987113,"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."}}