{"id":"W2154002389","doi":"10.1002/joc.1203","title":"Development of a hydrometeorological forcing data set for global soil moisture estimation","year":2005,"lang":"en","type":"article","venue":"International Journal of Climatology","topic":"Soil Moisture and Remote Sensing","field":"Environmental Science","cited_by":53,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"National Aeronautics and Space Administration","keywords":"Hydrometeorology; Forcing (mathematics); Climatology; Environmental science; Estimation; Data set; Meteorology; Precipitation; Geology; Mathematics; Statistics; Geography","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":[0.001307914,0.0004548288,0.0003159094,0.0008619266,0.0004522939,0.0003621054,0.000759895,0.000449181,0.001313274],"category_scores_gemma":[0.002613514,0.0003130772,0.0004633428,0.000782527,0.0001994517,0.0004271438,0.0004887687,0.0005859254,0.0003899344],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009118654,"about_ca_system_score_gemma":0.001035095,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02859421,"about_ca_topic_score_gemma":0.02335684,"domain_scores_codex":[0.9996806,0.0001102934,0.00003509181,0.00006628842,0.00008435093,0.00002335832],"domain_scores_gemma":[0.9985464,0.0003501396,0.0001502257,0.0004793538,0.0003907151,0.00008325457],"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":[0.0004474507,0.0008759234,0.1530733,0.0001362832,0.0002031628,0.0003496705,0.0002221473,0.7010925,0.02351957,0.002839217,0.01460802,0.1026327],"study_design_scores_gemma":[0.00056572,0.0001966136,0.1839559,0.00005637638,0.00006524413,0.00007885511,0.00007407286,0.7508982,0.04381933,0.001701331,0.0184555,0.0001328206],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8744977,0.00006416683,0.06407972,0.0003266819,0.0001406615,0.0006685866,0.05316247,0.002568063,0.00449198],"genre_scores_gemma":[0.8559263,0.00006409172,0.09350912,0.00007596998,0.00003776579,0.001074964,0.04840254,0.0002114622,0.0006978585],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02859421,"threshold_uncertainty_score":0.05685556,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0378119287720526,"score_gpt":0.3325494912552597,"score_spread":0.2947375624832071,"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."}}