{"id":"W3108140760","doi":"10.1190/tle39120883.1","title":"Using geodetic data in geothermal areas","year":2020,"lang":"en","type":"article","venue":"The Leading Edge","topic":"Geophysical Methods and Applications","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Natural Resources Canada","funders":"","keywords":"Geodetic datum; Geothermal gradient; Deformation monitoring; Geology; Interferometric synthetic aperture radar; Remote sensing; Geodesy; Field (mathematics); Geothermal energy; Synthetic aperture radar; Deformation (meteorology); Seismology; Geophysics","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":[0.00008962013,0.0000564009,0.00007922762,0.000009424425,0.00002947165,0.00001687909,0.0003177124,0.00001801463,0.00001538387],"category_scores_gemma":[0.0000291662,0.0000440178,0.00001243916,0.0001584843,0.00001906898,0.00004895932,0.00007221123,0.0001300769,0.0001741629],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001027309,"about_ca_system_score_gemma":0.00000458928,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000171991,"about_ca_topic_score_gemma":0.000003301383,"domain_scores_codex":[0.9996341,0.0000193473,0.0000764717,0.0001012494,0.00004253429,0.0001262608],"domain_scores_gemma":[0.999586,0.00006662134,0.000008352928,0.0003024269,0.000003324826,0.00003320695],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001676959,0.00008943607,0.00272912,0.0002804159,0.0001035634,0.00001755038,0.005788262,0.1831111,0.61423,0.01522202,0.006008232,0.1724035],"study_design_scores_gemma":[0.000119714,0.000005117173,0.00623697,0.00002310829,0.00001921718,0.000001920601,0.00004739869,0.9772111,0.0011918,0.001404028,0.01360131,0.0001383731],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9210725,0.0002521629,0.06958613,0.002387922,0.0001322634,0.0001926137,0.00002033692,0.0002441653,0.006111926],"genre_scores_gemma":[0.9951357,0.000003841681,0.004409354,0.0001867289,0.0002122736,0.000004190594,0.00000386406,0.00001683693,0.00002726239],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7940999,"threshold_uncertainty_score":0.223857,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1594074066558642,"score_gpt":0.3340051936914423,"score_spread":0.1745977870355781,"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."}}