{"id":"W1974239457","doi":"10.1117/12.918644","title":"Tunnel monitoring with an advanced InSAR technique","year":2012,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Synthetic Aperture Radar (SAR) Applications and Techniques","field":"Engineering","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"Golder Associates (Canada)","funders":"","keywords":"Interferometric synthetic aperture radar; Remote sensing; Parametric statistics; Synthetic aperture radar; Deformation monitoring; Exploit; Computer science; Residual; Scale (ratio); Pixel; Geology; Deformation (meteorology); Geography; Artificial intelligence; Cartography; Algorithm","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004277678,0.0003533368,0.0003608426,0.0001061874,0.00008079845,0.00006146968,0.0007531905,0.0002131268,0.00000764376],"category_scores_gemma":[0.00009686773,0.0002826064,0.000274764,0.0003330022,0.000151998,0.0008322638,0.00007845453,0.0003395283,0.000001114119],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001720514,"about_ca_system_score_gemma":0.00001434573,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004813782,"about_ca_topic_score_gemma":6.254562e-8,"domain_scores_codex":[0.9982817,1.077008e-8,0.0004911812,0.0002742284,0.0004841352,0.0004688119],"domain_scores_gemma":[0.9987808,0.00008363536,0.0001679181,0.00009902932,0.0006983622,0.0001702163],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00005578966,0.0001690719,0.0009133501,0.0004182539,0.0003040245,5.179349e-8,0.0002828574,0.00005305229,0.7261635,0.2626379,0.0005033842,0.008498782],"study_design_scores_gemma":[0.0005925334,0.0003278396,0.001447396,0.0004313341,0.0001383237,0.00004447958,0.001284294,0.007685644,0.9511889,0.0009554764,0.0353048,0.0005989885],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.9911526,0.0002048031,0.003715941,0.0003131647,0.0001511517,0.0007598529,0.0000237514,0.0004770331,0.003201657],"genre_scores_gemma":[0.4431908,0.00009226093,0.5558988,0.00001449653,0.0003404506,0.0003558334,0.000003793182,0.00007700405,0.00002647702],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5521829,"threshold_uncertainty_score":0.9999626,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009552134871760536,"score_gpt":0.2289827416821222,"score_spread":0.2194306068103617,"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."}}