{"id":"W4408840904","doi":"10.1109/jstars.2025.3553085","title":"SMAP Validation Experiment 2019–2022 (SMAPVEX19–22): Field Campaign to Improve Soil Moisture and Vegetation Optical Depth Retrievals in Temperate Forests","year":2025,"lang":"en","type":"article","venue":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","topic":"Soil Moisture and Remote Sensing","field":"Environmental Science","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph; Université du Québec à Trois-Rivières; Innovation and Economic Development Trois Rivières","funders":"Agricultural Research Service","keywords":"Vegetation (pathology); Environmental science; Temperate climate; Temperate forest; Water content; Remote sensing; Field (mathematics); Moisture; Hydrology (agriculture); Temperate rainforest; Soil science; Ecology; Geology; Meteorology; Ecosystem; Geography; Geotechnical engineering; Mathematics","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":[0.001943711,0.0009381594,0.0006277529,0.0004250082,0.001025983,0.0005090786,0.0007809615,0.0008632396,0.001295547],"category_scores_gemma":[0.001190568,0.0003530986,0.0004958863,0.0004670887,0.0005105881,0.0008483738,0.0007458352,0.001194702,0.0008811166],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008658317,"about_ca_system_score_gemma":0.001377626,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02007579,"about_ca_topic_score_gemma":0.02868403,"domain_scores_codex":[0.9994227,0.0001009099,0.00002654907,0.0001765423,0.0001797544,0.00009356696],"domain_scores_gemma":[0.9988292,0.00018994,0.0001119135,0.0002185491,0.0005018428,0.0001485496],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.007699284,0.01602123,0.2241592,0.000735038,0.0007991471,0.001254704,0.001108789,0.04638726,0.5257463,0.001614848,0.06175215,0.112722],"study_design_scores_gemma":[0.002705679,0.006835752,0.6974363,0.00009422759,0.0002503935,0.0005769168,0.0005261064,0.09265529,0.1445545,0.0006779568,0.05350351,0.0001833501],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9661932,0.000150308,0.01138969,0.0004222184,0.0001562056,0.0009270001,0.01307267,0.001226715,0.006462012],"genre_scores_gemma":[0.9162196,0.0001512048,0.03188437,0.0008248012,0.0001141422,0.001796833,0.04369302,0.0002782684,0.005037772],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02007579,"threshold_uncertainty_score":0.03991789,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01053606056867302,"score_gpt":0.2441002045508333,"score_spread":0.2335641439821602,"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."}}