{"id":"W646856622","doi":"","title":"Towards Improved Snow Water Equivalent Estimation via GRACE Assimilation","year":2011,"lang":"en","type":"article","venue":"NASA STI Repository (National Aeronautics and Space Administration)","topic":"Cryospheric studies and observations","field":"Earth and Planetary Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Snow; Environmental science; Data assimilation; Surface runoff; Snowmelt; Meteorology; Remote sensing; Climatology; Hydrology (agriculture); Geology; Geography","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002464443,0.0001895143,0.0001542338,0.00004152385,0.0006077568,0.0001167598,0.0001031356,0.0001007573,0.0005212121],"category_scores_gemma":[0.00006997481,0.000150628,0.00005546212,0.000119548,0.0001120177,0.0003890893,0.00002105883,0.0001208735,0.00002958502],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002802641,"about_ca_system_score_gemma":0.0001441106,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003378234,"about_ca_topic_score_gemma":0.0006368773,"domain_scores_codex":[0.9985706,0.00004210914,0.0003666444,0.0003215755,0.0004580145,0.0002410134],"domain_scores_gemma":[0.9992229,0.00008815603,0.0001379384,0.0001335112,0.0002838991,0.0001336239],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001040388,0.00102609,0.8168906,0.0002812428,0.00069617,0.00008926373,0.007149186,0.0112811,0.006875172,0.05029805,0.003213668,0.1011591],"study_design_scores_gemma":[0.0002683006,0.0002980999,0.8962359,0.00001110617,0.00004153005,0.00003637019,0.0001145468,0.09586107,0.001698065,0.003363773,0.001842869,0.0002283054],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9153546,0.0003938261,0.05183455,0.003160815,0.001365028,0.0006730674,0.00009950933,0.0001595871,0.02695898],"genre_scores_gemma":[0.9832178,0.00003364374,0.01516127,0.0001640617,0.0001308768,0.000009019389,0.0003365282,0.000006253809,0.0009405043],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1009308,"threshold_uncertainty_score":0.614243,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03198196373642659,"score_gpt":0.2391634061557841,"score_spread":0.2071814424193575,"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."}}