{"id":"W3207238794","doi":"10.1093/gji/ggab409","title":"Monitoring natural gas storage using Synthetic Aperture Radar: are the residuals informative?","year":2021,"lang":"en","type":"article","venue":"Geophysical Journal International","topic":"Synthetic Aperture Radar (SAR) Applications and Techniques","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"Natural Resources Canada","funders":"Lawrence Berkeley National Laboratory; Southern California Gas Company; California Energy Commission; U.S. Department of Energy","keywords":"Interferometric synthetic aperture radar; Geology; Residual; Synthetic aperture radar; Geodesy; Spatial variability; Remote sensing; Statistics; Algorithm","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001371182,0.0001907634,0.000186856,0.00006964869,0.0002485595,0.0002345605,0.0004053308,0.00007493471,0.00009313229],"category_scores_gemma":[0.0001150243,0.0001363547,0.000167507,0.000177969,0.00006521578,0.0003025999,0.00008766933,0.0007160275,0.00003280982],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001954017,"about_ca_system_score_gemma":0.00004375865,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005215065,"about_ca_topic_score_gemma":9.744776e-7,"domain_scores_codex":[0.9987745,0.00004639988,0.0003214002,0.0001373208,0.0004840838,0.0002363023],"domain_scores_gemma":[0.999042,0.0002831017,0.0001137554,0.0002315231,0.0002474257,0.00008216835],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00008521477,0.0004679244,0.001442895,0.0001143513,0.001925252,0.0005854391,0.003155899,0.001654382,0.0884831,0.01914319,0.01325111,0.8696913],"study_design_scores_gemma":[0.000751781,0.00003809293,0.0133768,0.0009569145,0.0001466807,0.004069282,0.0031937,0.1151332,0.09529678,0.0119984,0.7540156,0.001022742],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7277387,0.004028863,0.2424335,0.009779781,0.004805239,0.0003614902,0.00008984843,0.0006182736,0.01014438],"genre_scores_gemma":[0.9552306,0.0002654737,0.04270683,0.000196111,0.001361993,0.000008618731,0.000007624264,0.00003328351,0.0001894906],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8686685,"threshold_uncertainty_score":0.5560384,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01047943920128265,"score_gpt":0.2474244697213254,"score_spread":0.2369450305200428,"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."}}