{"id":"W2050638137","doi":"10.1007/s12665-013-2990-y","title":"Time-series analysis of subsidence associated with rapid urbanization in Shanghai, China measured with SBAS InSAR method","year":2013,"lang":"en","type":"article","venue":"Environmental Earth Sciences","topic":"Synthetic Aperture Radar (SAR) Applications and Techniques","field":"Engineering","cited_by":163,"is_retracted":false,"has_abstract":false,"ca_institutions":"Natural Resources Canada","funders":"","keywords":"Interferometric synthetic aperture radar; Urbanization; Subsidence; Groundwater; China; Geology; Physical geography; GNSS augmentation; Ground subsidence; Delta; Population; Synthetic aperture radar; Hydrology (agriculture); Geography; Mining engineering; Geomorphology; Remote sensing; Geotechnical engineering; Satellite","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":[0.0002477933,0.0001170455,0.0001971449,0.0001924166,0.00006613794,0.00002207875,0.0001542024,0.00003762824,0.0008405873],"category_scores_gemma":[0.00001011536,0.0000848922,0.00002663994,0.00107525,0.0003732494,0.0003009701,0.00001589386,0.0000582236,0.000007069934],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000221223,"about_ca_system_score_gemma":0.000009194073,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001301207,"about_ca_topic_score_gemma":0.00008196025,"domain_scores_codex":[0.9991527,0.00003744613,0.0001600845,0.000196799,0.0003042842,0.0001486678],"domain_scores_gemma":[0.999697,0.00005019536,0.00006446477,0.0001472853,0.000006409069,0.00003465214],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00002893223,0.0004795631,0.7650976,0.00002545293,0.001031086,0.000004961247,0.005172456,0.03720274,0.06019885,0.0006315832,0.0001326521,0.1299942],"study_design_scores_gemma":[0.0001364216,0.0001591544,0.8559706,0.00003705364,0.0001055294,0.000002500658,0.0003597792,0.08559085,0.05696706,0.00009028237,0.00035552,0.0002252717],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9105589,0.0001658558,0.08510094,0.00005908451,0.000006493487,0.0003026924,0.00001192178,0.00009335268,0.003700765],"genre_scores_gemma":[0.8936641,0.00002358836,0.1061743,0.00000918415,0.000003207628,0.00002381959,0.00001338493,0.000008926145,0.00007940584],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1297689,"threshold_uncertainty_score":0.9203842,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005107774791332725,"score_gpt":0.1847151394080365,"score_spread":0.1796073646167037,"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."}}