{"id":"W2135398221","doi":"10.5194/isprsarchives-xl-1-81-2014","title":"Ground deformation monitoring using RADARSAT-2 DInSAR-MSBAS at the Aquistore CO<sub>2</sub> storage site in Saskatchewan (Canada)","year":2014,"lang":"en","type":"article","venue":"˜The œinternational archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciences","topic":"Synthetic Aperture Radar (SAR) Applications and Techniques","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Geological Survey of Canada; Natural Resources Canada","funders":"","keywords":"Geology; Piezometer; Synthetic aperture radar; Interferometric synthetic aperture radar; Tiltmeter; Remote sensing; Satellite; Induced seismicity; Global Positioning System; Geodesy; Deformation monitoring; Differential GPS; Geodetic datum; Seismology; Deformation (meteorology); Geotechnical engineering; Aquifer; Groundwater","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001629839,0.0002834141,0.0001325648,0.0006525077,0.0005059007,0.0003825155,0.0003819093,0.0001849896,0.0009403197],"category_scores_gemma":[0.0001976647,0.0001446108,0.00009763539,0.001430902,0.0003245909,0.0001980675,0.0002763051,0.0002119601,0.0002259097],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003534222,"about_ca_system_score_gemma":0.003830591,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.8433684,"about_ca_topic_score_gemma":0.9626644,"domain_scores_codex":[0.999881,0.0000103921,0.000004864739,0.00003186781,0.00004271662,0.00002913123],"domain_scores_gemma":[0.9997069,0.00001582811,0.00003403511,0.00002199306,0.0001657644,0.00005532749],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003362851,0.0001703664,0.9085135,0.00006106169,0.0001020978,0.0005258704,0.0008225346,0.009731402,0.03485949,0.0001893317,0.003875462,0.04081256],"study_design_scores_gemma":[0.00001803865,0.0000260554,0.9864615,0.00000784143,0.00001230634,0.00004079434,0.0007503625,0.007995558,0.002933521,0.00002866968,0.00170972,0.00001562609],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9941916,0.00002982719,0.0004832704,0.00003977374,0.000003614755,0.00003475465,0.003209303,0.00006094782,0.001946841],"genre_scores_gemma":[0.9925172,0.00004501914,0.001775187,0.00003355947,0.000001797881,0.00002435833,0.003699861,0.00001243305,0.001890635],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1566316,"threshold_uncertainty_score":0.315108,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01095515119673441,"score_gpt":0.2281224238022215,"score_spread":0.2171672726054871,"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."}}