{"id":"W2905544248","doi":"10.20944/preprints201812.0136.v1","title":"Analysis of Spatiotemporal Variation of Land Subsidence in Beijing Plain, China","year":2018,"lang":"en","type":"preprint","venue":"Preprints.org","topic":"Synthetic Aperture Radar (SAR) Applications and Techniques","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Key Research and Development Program of China; China Geological Survey; Canadian Space Agency; National Natural Science Foundation of China; European Space Agency; National Aeronautics and Space Administration","keywords":"Beijing; Geology; Alluvial plain; Groundwater-related subsidence; Subsidence; Groundwater; Land reclamation; Hydrology (agriculture); Hydrogeology; Alluvium; China; Geomorphology; Geography; Geotechnical engineering","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002942311,0.0002553239,0.0002710479,0.001742064,0.0003312954,0.0004551564,0.0002777599,0.0001959389,0.0006925146],"category_scores_gemma":[0.000608406,0.0001336402,0.000258388,0.002394635,0.0002436704,0.000325836,0.0003749784,0.0001122066,0.0001287123],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007294338,"about_ca_system_score_gemma":0.000490691,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03929217,"about_ca_topic_score_gemma":0.04284729,"domain_scores_codex":[0.9997985,0.00001819525,0.00002181211,0.00006075464,0.00005609879,0.00004467803],"domain_scores_gemma":[0.9997014,0.00003406435,0.00007755823,0.00003211563,0.00008513825,0.00006968585],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00008015772,0.0000368187,0.9765597,0.00004981118,0.00009310649,0.0006349142,0.0004782747,0.003156717,0.003678043,0.0002520922,0.0007925789,0.01418776],"study_design_scores_gemma":[0.000002262899,0.000009196051,0.9965437,0.000001937403,0.00000897257,0.00003819666,0.00012613,0.002807289,0.0001065886,0.00002046227,0.0003316272,0.000003488001],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9986516,0.00006583382,0.0001824731,0.0000300316,0.000002701462,0.000008034402,0.0005469982,0.00001751804,0.0004948197],"genre_scores_gemma":[0.9987612,0.00003964194,0.0001378235,0.000004232221,0.000002878908,0.000007935778,0.0007937716,0.000002618989,0.0002499973],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03929217,"threshold_uncertainty_score":0.07812691,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03962963897024947,"score_gpt":0.3003496812006734,"score_spread":0.2607200422304239,"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."}}