{"id":"W7117645397","doi":"10.1080/17538947.2025.2609469","title":"A seamless global daily soil moisture dataset (2010–2015) harmonized from SMOS observations and SMAP-era assimilation modeling","year":2025,"lang":"en","type":"article","venue":"International Journal of Digital Earth","topic":"Soil Moisture and Remote Sensing","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ministry of Education and Child Care","funders":"China Postdoctoral Science Foundation; Postdoctoral Research Foundation of China; National Natural Science Foundation of China","keywords":"Data assimilation; Water content; Satellite; Terrain; Mean squared error; Precipitation; Brightness temperature; Earth observation; Principal component analysis","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004843299,0.0004865703,0.0003201649,0.0008677635,0.0002433303,0.0003348693,0.0006132445,0.0004023914,0.001251804],"category_scores_gemma":[0.0008262805,0.0001690888,0.0005963217,0.001445565,0.0002068885,0.0005579152,0.0006053714,0.0004556978,0.0007496957],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004315017,"about_ca_system_score_gemma":0.0009992617,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03003661,"about_ca_topic_score_gemma":0.05255777,"domain_scores_codex":[0.9997317,0.00003331569,0.00003064302,0.0000956482,0.00007692867,0.00003177096],"domain_scores_gemma":[0.9995798,0.0000303932,0.00006544597,0.000117005,0.0001674013,0.0000399192],"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":[0.001041746,0.0009309893,0.3713099,0.00117021,0.00126371,0.0007590955,0.0005961503,0.1450035,0.05375202,0.006299758,0.2193527,0.1985202],"study_design_scores_gemma":[0.00026698,0.0001603657,0.6503475,0.0001235144,0.0001701545,0.0002227413,0.0003568637,0.2017963,0.01672633,0.001689222,0.1279866,0.0001534069],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"dataset","genre_scores_codex":[0.5106086,0.0004087769,0.02148277,0.0004852793,0.0002408245,0.0002177755,0.4560569,0.003450475,0.007048666],"genre_scores_gemma":[0.3886069,0.0001790273,0.0258594,0.0001517982,0.00006267318,0.000221565,0.583468,0.0002014988,0.001249232],"genre_candidate":"dataset","genre_consensus":null,"teacher_disagreement_score":0.03003661,"threshold_uncertainty_score":0.05972356,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02150389718365685,"score_gpt":0.2634944173907741,"score_spread":0.2419905202071172,"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."}}