{"id":"W2985855551","doi":"10.1029/2018wr024581","title":"Multiscale Data Fusion for Surface Soil Moisture Estimation: A Spatial Hierarchical Approach","year":2019,"lang":"en","type":"article","venue":"Water Resources Research","topic":"Soil Moisture and Remote Sensing","field":"Environmental Science","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Office of the Director; National Aeronautics and Space Administration","keywords":"Environmental science; Sensor fusion; Watershed; Spatial analysis; Remote sensing; Spatial variability; Bayesian probability; Computer science; Data mining; Statistics; Geography; Mathematics; Machine learning; Artificial intelligence","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001494638,0.0001628625,0.0001986101,0.00005608836,0.0004019852,0.0001508276,0.0008843935,0.0001716975,0.000225042],"category_scores_gemma":[0.00007047263,0.0001021849,0.00005605743,0.0001916811,0.000340691,0.0001810452,0.001744315,0.0005136548,0.0009838264],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009331489,"about_ca_system_score_gemma":0.000009767392,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004349304,"about_ca_topic_score_gemma":0.00100512,"domain_scores_codex":[0.9969534,0.0002697195,0.0002149256,0.0007990537,0.00105157,0.0007112832],"domain_scores_gemma":[0.9985466,0.000186328,0.00002636508,0.001048603,0.00002806914,0.0001640321],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001753379,0.00109263,0.09848453,0.0005795237,0.0001237057,0.00006066063,0.03414715,0.1026081,0.2518727,0.00003163381,0.04588545,0.4633605],"study_design_scores_gemma":[0.001125465,0.0001974181,0.0347827,0.00003764083,0.00001163994,0.0000274797,0.0003672698,0.7437039,0.008996844,0.000404211,0.2100144,0.0003309798],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9685485,0.00004062893,0.00275455,0.001127136,0.0001103678,0.0008949679,0.00001548661,0.00004989246,0.02645844],"genre_scores_gemma":[0.9792004,0.000005290047,0.01166618,0.00006202079,0.0002073881,0.000004535539,0.0003523279,0.0000342983,0.00846759],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6410958,"threshold_uncertainty_score":0.999794,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04845510862414651,"score_gpt":0.3137326781079339,"score_spread":0.2652775694837874,"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."}}