{"id":"W4312724177","doi":"10.1109/igarss46834.2022.9883975","title":"Assessing the Temporal Dynamics of Terrestrial Water Storage in Ten Large River Basins in China","year":2022,"lang":"en","type":"article","venue":"IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium","topic":"Geophysics and Gravity Measurements","field":"Earth and Planetary Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia, Okanagan Campus; University of British Columbia","funders":"","keywords":"Drainage basin; China; Water resources; Environmental science; Structural basin; Water storage; Hydrology (agriculture); Climate change; Climatology; Water resource management; Geology; Geography; Oceanography; Ecology; Geomorphology","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":[0.001511192,0.000145685,0.0001834872,0.0002661432,0.0003798642,0.0001377756,0.0004074275,0.00003379314,0.0001078511],"category_scores_gemma":[0.00002396592,0.000104774,0.00006238063,0.0004126448,0.0001808575,0.0003390758,0.0001191723,0.0004145157,0.000003334641],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006158524,"about_ca_system_score_gemma":0.00008783668,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01410602,"about_ca_topic_score_gemma":0.01232446,"domain_scores_codex":[0.9977957,0.0002379836,0.0003495575,0.0003771276,0.0008753033,0.0003643075],"domain_scores_gemma":[0.9995514,0.00006169867,0.0001340037,0.0001729925,0.00002886313,0.00005109035],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003546866,0.0004613572,0.6899834,0.00005058827,0.00009546705,0.0007252966,0.01558333,0.06638645,0.02481557,0.0001352991,0.0003073726,0.2011012],"study_design_scores_gemma":[0.0005913684,0.00007700863,0.3447005,0.00003403929,0.000007442078,0.00004224128,0.0009068757,0.6507798,0.000182938,0.001445237,0.001051463,0.0001811354],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.993984,0.00003004179,0.0003031915,0.001818671,0.002706112,0.0001754925,0.0002088936,0.000008306393,0.0007653193],"genre_scores_gemma":[0.9988632,0.00001884552,0.000272521,0.0001791069,0.0000966027,1.868223e-7,0.0001795065,0.000004993723,0.0003850949],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5843933,"threshold_uncertainty_score":0.9924591,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01488639454882381,"score_gpt":0.2490740744658941,"score_spread":0.2341876799170702,"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."}}