{"id":"W4297237166","doi":"10.5194/gstm2022-57","title":"Comparing Land Surface Model Performance between Fine-scale and Coarse-scale Assimilation: Leaf Area Index Retrievals versus GRACE / GRACE-FO Retrievals ","year":2022,"lang":"en","type":"preprint","venue":"","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Data assimilation; Environmental science; Leaf area index; Water content; Atmospheric sciences; Meteorology; Geography; Physics; Agronomy","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008024487,0.0004855687,0.0003498956,0.0002767515,0.0002717359,0.0005740219,0.0005913768,0.0006170981,0.001243739],"category_scores_gemma":[0.00137959,0.0001624858,0.0005831476,0.0003733053,0.0002547207,0.0008725259,0.0004256145,0.0005493648,0.0004300923],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004080021,"about_ca_system_score_gemma":0.0005568959,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02807766,"about_ca_topic_score_gemma":0.02299345,"domain_scores_codex":[0.9997551,0.00004220973,0.00002271254,0.00008418732,0.0000604177,0.00003535801],"domain_scores_gemma":[0.9993929,0.000171466,0.00005110029,0.0001706748,0.0001682139,0.00004568376],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.004145834,0.00108345,0.077451,0.0002077388,0.000714846,0.0001614618,0.0002086872,0.7040404,0.09539098,0.001256017,0.007712632,0.107627],"study_design_scores_gemma":[0.0001802598,0.000296959,0.0646522,0.000008956676,0.00006513613,0.0000243608,0.00006815384,0.9179592,0.01541753,0.0002360664,0.001039425,0.0000516784],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9863624,0.0001642972,0.007262801,0.0002192284,0.00008290073,0.00003297679,0.00123509,0.001193478,0.003446901],"genre_scores_gemma":[0.9903042,0.00005535408,0.006284882,0.00008690449,0.00001751234,0.00002541134,0.002381424,0.0001215418,0.0007228193],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02807766,"threshold_uncertainty_score":0.05582845,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06875491243732657,"score_gpt":0.2742284204206417,"score_spread":0.2054735079833152,"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."}}