{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004639323,0.0001556425,0.0002577839,0.0002104155,0.0001305987,0.00007893536,0.00006275115,0.000166052,9.874058e-7],"category_scores_gemma":[0.0001850525,0.0001390174,0.00002835868,0.0007774933,0.00004945277,0.0001145127,0.00004395022,0.0004084636,8.022477e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001424536,"about_ca_system_score_gemma":0.00007074227,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003941995,"about_ca_topic_score_gemma":0.008116622,"domain_scores_codex":[0.9986618,0.00006344145,0.0005248691,0.000251185,0.0002729991,0.0002257492],"domain_scores_gemma":[0.9994212,0.0001287128,0.00015086,0.0001276115,0.00008702924,0.00008457163],"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":[0.0001367238,0.00004819808,0.01713878,0.00004166674,0.00002828032,0.00002519684,0.001618029,0.00588975,0.5744091,0.0001150853,0.00023078,0.4003184],"study_design_scores_gemma":[0.0009752928,0.0001325629,0.8309298,0.0002769432,0.00003140472,0.00003356934,0.0002701768,0.01321765,0.1517963,0.001701669,0.0004152713,0.0002193825],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9868492,0.0001604173,0.009101744,0.001218131,0.0003259011,0.000290512,2.583241e-7,0.00001015049,0.002043723],"genre_scores_gemma":[0.9714342,0.0001212679,0.02772424,0.0004533219,0.000116304,1.195782e-7,0.000002459707,0.000009794644,0.0001383086],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.813791,"threshold_uncertainty_score":0.5668965,"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."}}