{"id":"W2003962096","doi":"10.1109/tgrs.2014.2364913","title":"The Soil Moisture Active Passive Validation Experiment 2012 (SMAPVEX12): Prelaunch Calibration and Validation of the SMAP Soil Moisture Algorithms","year":2014,"lang":"en","type":"article","venue":"IEEE Transactions on Geoscience and Remote Sensing","topic":"Soil Moisture and Remote Sensing","field":"Environmental Science","cited_by":278,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa; University of Manitoba; Agriculture and Agri-Food Canada; University of Guelph; Environment and Climate Change Canada; Université de Sherbrooke; Stantec (Canada)","funders":"","keywords":"Environmental science; Remote sensing; Water content; Satellite; Calibration; Radar; Meteorology; Moisture; Computer science; Geology; Engineering","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.000401254,0.0002361173,0.0001814242,0.00005129175,0.00155653,0.0001298853,0.0001468004,0.0001644848,0.000004257328],"category_scores_gemma":[0.00003108475,0.0001412957,0.00008699178,0.0003814199,0.0007844644,0.0004109856,0.00001605873,0.0003321589,0.000003117791],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001065224,"about_ca_system_score_gemma":0.00003008284,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002957978,"about_ca_topic_score_gemma":0.001999905,"domain_scores_codex":[0.9981048,0.0002579724,0.0002940045,0.0004821497,0.0005554924,0.0003055955],"domain_scores_gemma":[0.9990464,0.0002116271,0.0002265563,0.000373772,0.00004104089,0.0001005417],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00003933976,0.00003580365,0.00002846147,0.00001008732,0.00001862565,9.011883e-7,0.003020344,0.006893984,0.07165746,0.000008773805,0.00006152123,0.9182247],"study_design_scores_gemma":[0.000474999,0.000155331,0.01836433,0.0001321732,0.00009865225,0.00007494605,0.001547169,0.1939252,0.783422,0.0007912383,0.0006767344,0.0003371779],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5856717,0.00006040392,0.4098113,0.001812823,0.001035444,0.0004121235,0.00000362372,0.00003994375,0.001152743],"genre_scores_gemma":[0.9960653,0.000128391,0.002930127,0.0001781195,0.00008355213,3.821469e-7,0.000001989899,0.00001660741,0.0005955564],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9178875,"threshold_uncertainty_score":0.9997433,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007742084051347676,"score_gpt":0.2169056922544043,"score_spread":0.2091636082030566,"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."}}