Random forest regression using autonomous in situ ocean observations: Inferring small-scale variability during a Southern Ocean field experiment - Data

<bgcArgo_datasetB> BGC-Argo data used for RFR training (9 floats) and validation (SOGOS float) <goship_dataset_i06_i07> GO-SHIP data for RFR training (I06) and testing (I07) <sogos_float5906030_RFRvalidation> RFR validation errors for the SOGOS float (WMO ID: 5906030) with model H parameters <sogos_glider659_RFRpred> Depth-resolved nitrate estimates for the SOGOS Seagliders (SG659 and SG660), derived using the SOGOS RFR with model H parameters.<bgcArgo_datasetB> 用于随机森林回归（Random Forest Regression，RFR）训练（含9个浮标）与验证（采用SOGOS浮标）的生物地球化学Argo（BGC-Argo）数据 <goship_dataset_i06_i07> 用于随机森林回归（RFR）训练（I06航次）与测试（I07航次）的全球海洋船舶水文调查计划（Global Ocean Ship-based Hydrographic Investigations Program，GO-SHIP）数据 <sogos_float5906030_RFRvalidation> 采用模型H参数计算得到的SOGOS浮标（世界气象组织（World Meteorological Organization，WMO）编号：5906030）RFR验证误差 <sogos_glider659_RFRpred> 通过搭载模型H参数的SOGOS RFR模型推导得到的SOGOS Seaglider型海洋滑翔机（SG659与SG660）深度分辨硝酸盐浓度估算结果