{"id":"W4396240719","doi":"10.1007/s10064-024-03680-3","title":"Application of Sentinel-1 InSAR to monitor tailings dams and predict geotechnical instability: practical considerations based on case study insights","year":2024,"lang":"en","type":"article","venue":"Bulletin of Engineering Geology and the Environment","topic":"Synthetic Aperture Radar (SAR) Applications and Techniques","field":"Engineering","cited_by":25,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University; Precision Nanosystems (Canada); BGC Engineering (Canada); University of British Columbia; Klohn Crippen Berger (Canada); University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; University of Waterloo","keywords":"Nature Conservation; Tailings; Interferometric synthetic aperture radar; Geotechnical engineering; Geology; Instability; Mining engineering; Remote sensing; Synthetic aperture radar","routes":{"ca_aff":true,"ca_fund":true,"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":[0.002167259,0.000591394,0.0002759329,0.0009977425,0.0005023236,0.0007339449,0.000787463,0.0009312433,0.0009053481],"category_scores_gemma":[0.002992389,0.0001644684,0.0003532705,0.0005806183,0.0007316947,0.000821809,0.0005060997,0.000380601,0.0002120535],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005104452,"about_ca_system_score_gemma":0.0005340033,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005050165,"about_ca_topic_score_gemma":0.01291988,"domain_scores_codex":[0.9993693,0.0002449944,0.00003664761,0.00008454627,0.0001886147,0.00007590888],"domain_scores_gemma":[0.9976046,0.001315887,0.0002426771,0.0002615086,0.0004641617,0.0001111044],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.0005033261,0.0006932896,0.3858344,0.0003898263,0.000151242,0.01081769,0.001708661,0.4222666,0.03142298,0.007073307,0.007542526,0.1315961],"study_design_scores_gemma":[0.00004216194,0.0006302907,0.047895,0.0001423308,0.0001090984,0.002373209,0.003984738,0.9018805,0.03027915,0.006955911,0.005624415,0.0000831647],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9481122,0.0001559893,0.04648108,0.0006370859,0.00003143467,0.0001106658,0.0004439362,0.0003489192,0.003678655],"genre_scores_gemma":[0.9636356,0.00009889949,0.03556773,0.00003740579,0.000008826752,0.00002709794,0.0001964077,0.00002030284,0.0004077267],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005050165,"threshold_uncertainty_score":0.01146168,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005484769724306883,"score_gpt":0.216433108157461,"score_spread":0.2109483384331541,"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."}}