{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003212741,0.0009687541,0.0005588186,0.0004972386,0.0009655195,0.0005490164,0.001400232,0.0008189529,0.001016635],"category_scores_gemma":[0.002929498,0.0004142225,0.0004619144,0.0006311051,0.0006883931,0.0007397439,0.001079968,0.001066533,0.0007418564],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001325016,"about_ca_system_score_gemma":0.00241142,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.05916381,"about_ca_topic_score_gemma":0.09170793,"domain_scores_codex":[0.9987588,0.0002594286,0.00005427205,0.0003152118,0.0004779344,0.0001343349],"domain_scores_gemma":[0.9980195,0.0002787458,0.0001460428,0.0005265423,0.0008922496,0.0001368757],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.005389898,0.008024488,0.1639981,0.0004410635,0.0006118692,0.0005069788,0.0008339378,0.3496803,0.1552739,0.003588676,0.0408615,0.2707894],"study_design_scores_gemma":[0.002472752,0.004644703,0.2429854,0.00008559797,0.0001317071,0.000338562,0.000381894,0.5633749,0.1423611,0.001202558,0.04183465,0.0001862842],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.917556,0.0001629301,0.05951747,0.0001346351,0.00009137215,0.001278478,0.01263029,0.002695361,0.005933467],"genre_scores_gemma":[0.820801,0.00008909836,0.1358054,0.0001698013,0.00002217559,0.001465927,0.03666933,0.0005118921,0.004465322],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05916381,"threshold_uncertainty_score":0.1176389,"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."}}