{"id":"W3087909386","doi":"10.1029/2020gl089697","title":"SMAP Detects Soil Moisture Under Temperate Forest Canopies","year":2020,"lang":"en","type":"article","venue":"Geophysical Research Letters","topic":"Soil Moisture and Remote Sensing","field":"Environmental Science","cited_by":77,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Trois-Rivières","funders":"Goddard Space Flight Center; Jet Propulsion Laboratory; National Aeronautics and Space Administration","keywords":"Environmental science; Temperate forest; Temperate rainforest; Radiometry; Water content; Temperate climate; Remote sensing; Atmospheric sciences; Biogeochemical cycle; Vegetation (pathology); Brightness temperature; Brightness; Ecosystem; Geology; Ecology","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0001845337,0.0001815487,0.0001880884,0.0000337368,0.0003622021,0.0001141252,0.0003546311,0.00007988465,0.00006271774],"category_scores_gemma":[0.0002013232,0.0001432644,0.00009767974,0.0005990083,0.0006539397,0.0001509671,0.0004025037,0.000775354,0.002187854],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001582463,"about_ca_system_score_gemma":0.00002708069,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005922724,"about_ca_topic_score_gemma":0.003308505,"domain_scores_codex":[0.9973094,0.0001955723,0.0001447635,0.0005264741,0.001037278,0.0007864505],"domain_scores_gemma":[0.9990675,0.0002119427,0.00002606142,0.00028197,0.000021344,0.000391226],"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":[0.00008611662,0.00006051203,0.0117401,0.00002888746,0.0000378413,0.0001706437,0.001129945,0.004920047,0.916039,0.0001472971,0.05540239,0.01023724],"study_design_scores_gemma":[0.0008576784,0.000338391,0.9299782,0.00004827634,0.00002034023,0.00000976,0.0006634243,0.004242691,0.03819684,0.001768936,0.02320536,0.0006701255],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9445645,0.00002065409,0.0001324122,0.04821644,0.0000984784,0.0001894658,0.000001182754,0.00006990703,0.006706999],"genre_scores_gemma":[0.9895313,0.000006821461,0.000142901,0.009374601,0.0005696383,0.000004039238,0.000005040919,0.00002891243,0.0003367111],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9182381,"threshold_uncertainty_score":0.998589,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03271219877281981,"score_gpt":0.2757603888741011,"score_spread":0.2430481901012813,"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."}}