{"id":"W2347893477","doi":"","title":"Monitoring land subsidence based on PSInSAR","year":2012,"lang":"en","type":"article","venue":"","topic":"Synthetic Aperture Radar (SAR) Applications and Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"CAE (Canada)","funders":"","keywords":"Groundwater; Subsidence; Overexploitation; Ground subsidence; Beijing; Geology; Water level; Cone of depression; Interferometric synthetic aperture radar; Hydrology (agriculture); Interferometry; Environmental science; Remote sensing; Aquifer; Geodesy; Geography; Geomorphology; Cartography; Mining engineering; Geotechnical engineering; Groundwater recharge; Synthetic aperture radar","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002217113,0.0002459252,0.0002386197,0.001084645,0.0001188083,0.0002245932,0.000223417,0.0002013731,0.0003963998],"category_scores_gemma":[0.0002934005,0.0001118118,0.0001285291,0.0009358177,0.0001526115,0.0003233896,0.0002482074,0.0001326837,0.0001505526],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002071814,"about_ca_system_score_gemma":0.000189791,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002979597,"about_ca_topic_score_gemma":0.007752297,"domain_scores_codex":[0.9998034,0.00002343878,0.00001214709,0.0000514959,0.00007600337,0.0000335704],"domain_scores_gemma":[0.9998122,0.00001991304,0.00005304045,0.00001839008,0.00007898385,0.0000174584],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003805395,0.0002953532,0.5608716,0.0002087287,0.0001262294,0.001224118,0.000589254,0.02963104,0.1845227,0.0006424437,0.002442162,0.2190658],"study_design_scores_gemma":[0.00002895034,0.00023412,0.7916391,0.00001791538,0.0000861221,0.00034107,0.0004595271,0.1857715,0.01891892,0.0001709127,0.002300563,0.00003124092],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9911411,0.00007907997,0.006123676,0.00002639555,0.00001235611,0.00002176417,0.0003250458,0.0001584038,0.002112158],"genre_scores_gemma":[0.9951789,0.0000572323,0.004162619,0.000009615749,0.000005841966,0.000009214802,0.0003399479,0.000003901819,0.0002327821],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002979597,"threshold_uncertainty_score":0.005924523,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01302333617914657,"score_gpt":0.2330792149729922,"score_spread":0.2200558787938456,"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."}}