{"id":"W4388660971","doi":"10.1117/1.jrs.17.044511","title":"Spatio-temporal analysis and volumetric characterization of interferometric synthetic aperture radar-observed deformation signatures related to underground and in situ leach mining","year":2023,"lang":"en","type":"article","venue":"Journal of Applied Remote Sensing","topic":"Synthetic Aperture Radar (SAR) Applications and Techniques","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Pacific Northwest National Laboratory; European Space Agency","keywords":"Geology; Interferometric synthetic aperture radar; Synthetic aperture radar; Subsidence; Groundwater-related subsidence; Interferometry; Inversion (geology); Mining engineering; Remote sensing; Seismology; Geomorphology; Tectonics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001593342,0.0001853607,0.000141599,0.001030513,0.0001148997,0.0002425918,0.0002474789,0.0001854799,0.0003744221],"category_scores_gemma":[0.0005345675,0.0001124522,0.0001726432,0.0009183086,0.0002289618,0.0001873185,0.0001905503,0.0001618457,0.0000804226],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002575245,"about_ca_system_score_gemma":0.0002324294,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00805662,"about_ca_topic_score_gemma":0.01691954,"domain_scores_codex":[0.9999133,0.000008462211,0.00000573832,0.00002075155,0.00002928594,0.00002235883],"domain_scores_gemma":[0.9997333,0.00004556694,0.0001041943,0.00003209252,0.00006357296,0.00002134134],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003759878,0.0003595168,0.6505699,0.0001495076,0.0001481384,0.0006075255,0.0006948575,0.06385811,0.2024583,0.001069363,0.00116256,0.07854625],"study_design_scores_gemma":[0.000009723235,0.00007079936,0.8954097,0.000009801365,0.00002666791,0.000236149,0.0002362316,0.09341258,0.009804297,0.0002103576,0.0005536959,0.00002007475],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9966539,0.00002673513,0.002303585,0.00002829124,0.000003040914,0.000005959165,0.0003268297,0.00004210329,0.0006094156],"genre_scores_gemma":[0.9981204,0.00002266502,0.001319691,0.000007235525,0.000003981233,0.000004169672,0.0003874922,0.000006205024,0.0001282157],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00805662,"threshold_uncertainty_score":0.0160194,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009993665750046782,"score_gpt":0.2149168467352582,"score_spread":0.2049231809852114,"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."}}