dataesr/scientific-paragraphs-categorization huggingface dataset:
Horizontal weighting in the footprint area In this work we make use of three horizontal weighting functions to average soil moisture measurements at distances r from the CRNS probe. First, the equal average (weights equal 1), which was usually applied for validation with soil moisture networks and remote sensing products. It was also used for calibration datasets if locations were arranged according to the COSMOS standard sampling scheme, (25 m, 75 m, 200 m), such that the samples automatically represent areas of equal contribution to the neutron signal. These calculations were based on a simple exponential sensitivity function  (Zreda et al. , 2008)  and presented by  Franz et al. (2012a)  and  Zreda et al. (2012). Second, the conventional weighting approach uses an (almost) exponential sensitivity function based on Monte-Carlo simulations from  Zreda et al.
Performance Measures To evaluate the performance of time series and calibration data, we apply prominent measures used in environmental and hydrological research. The robustness of this approach is evaluated by applying different performance measures, which is a common strategy to falsify new methodological approaches (see e. g. ,  Glaser et al. , 2016). Popular efficiency measures are the Nash-Sutcliffe-Efficiency (NSE)  (Nash and Sutcliffe, 1970)  and the more modern Kling-Gupta-Efficiency (KGE)  (Gupta et al. , 2009) , while the Root-Mean-Square-Error (RMSE) and the Pearson correlation coefficient (ρ) are well-established standard approaches. NSE = 1 - (A -B) 2 (B -B ) 2 KGE = 1 - ρ (A, B) -1 2 + σ A σ B -1 2 + A B -1 2 1 2 RMSE = (A -B) 2 1 2 ρ = (A -A ) (B -B ) σ A σ B where A = θ(N, N 0 ) denotes the water equivalent measured by the CRNS (N 0 needs to be calibrated), B denotes the actual field soil water equivalent, θ, measured by independent instruments, and x = 1 n n 1 x denotes the average (expected value) of a set of data points x.
In the ideal case of optimal agreement between the variables A and B, the measures would reach NSE = 1, KGE = 1, RMSE = 0 , and ρ = 1. NSE normalizes the mean squared error by the observed variance, where the mean observed variable B is used as a baseline. Following this approach, site-specific variations could translate to biased estimation of model skills among different sites. On the other hand, the KGE measure is a revised version of NSE that allows to analyze the relative importance of linear correlation ρ, variability σ, and bias • of simulated and observed variables  (Gupta et al. , 2009). RMSE is simply a measure of the differences between two time series but is prone to biased datasets and outliers. The correlation ρ is an accepted approach in experimental geophysics to identify similar or unknown effects (e.
g. ,  Fu et al. , 2015)  in two time series. However, if many factors could explain a single observation, only using the correlation measure may lead to false recognition of coincidental effects. The KGE is the most appropriate performance measure for time series data as it combines three distinct measures to optimally account for absolute errors and anomalies (compare also  Heidbüchel et al. , 2016). In the following analysis, we have thus optimized the KGE value between the CRNS and the independent soil moisture data to find a single calibration parameter N 0 per site.
Study sites In order to provide a robust falsification of a potential benefit when using the revised weighted-averaging approach, datasets of six distinct sites have been consulted that offer comparison of the CRNS with independent soil moisture data under various climatic conditions (Fig. 3 ). The Sheepdrove Organic Farm in Lambourn (UK). The Sheepdrove Organic Farm is located at (51. 528175, -1. 467311, 190 m asl) in the Lambourn catchment in South England. This region is characterized by a temperate climate with yearly average precipitation of 815 mm, evenly distributed over the year, and with a mean daily maximum temperature of 14 • C. The CRNS probe is located at a grass stripe which exhibits unmanaged soil and vegetation cover. The surrounding field is grazed by sheep during several variable periods throughout the year.
, 2014). Complex General note: The experimental data collected in these sites can be classified with the following terminology. Calibration datasets typically denote soil samples taken at a single point in time, which are then analyzed for soil water content in the lab and used to find a calibration parameter N 0 for the specific site. Notwithstanding, by repeating the sampling on other days also a validation can be addressed to some extent. Validation datasets typically denote time series measurements of a soil moisture monitoring network (SoilNet,  Bogena et al. (2010) ). They are used to validate the performance of the CRNS sensor, but can be also used to find the calibration parameter N 0 with the help of so-called performance measures. In the present work, we are using the terms calibration site and validation site only to distinguish between temporally distinct samples and continuous data of independent soil moisture measurements, although in both cases those datasets are used to calibrate the CRNS probe.
Improvement of the calibration performance In the farmlands of Great Britain, managed fields are often divided by stripes of hedges or unmanaged grassland. While unmanaged patches appear to be ideal positions for environmental-monitoring equipment, the presented example shows that CRNS measurements can be biased from the intended information about the field site. Three calibration datasets were collected at various wetness conditions within 9 months. The sampling design was based on the COSMOS standard sampling scheme at 25, 75, and 200 m, plus an additional location at 1 m near the CRNS probe. Fig. 4  demonstrates how the equal (red) and conventional (orange) weighting of the three calibration datasets deviate significantly from the single theoretical relation N (θ)  (Desilets et al. , 2010).
Furthermore, the experiment clearly shows the importance of a proper positioning of the CRNS probe. Insights from the British grassland have also been confirmed with calibration datasets from an the agricultural site near Braunschweig. Scheiffele (2015) used the COSMOS standard sampling scheme for three calibration campaigns during the agricultural season 2014 in May (no crop), July (maximum water content in biomass), and October (after harvest). Using the conventional averaging approach (orange), the calibration data in Fig. 5  could be interpreted as showing a reduced amount of hydrogen pools in the CRNS signal during the period of growing maize. This effect seems to be even higher in October than in July. This is opposed to observations by  Franz et al. (2013b)  and  Baroni and Oswald (2015)  who confirmed that the water contained in crop biomass and below-ground residues typically adds to the apparent soil water equivalent.
The data weighted with the revised functions demonstrates that the lines infered from the calibration points converge much closer to a single theoretical line  (Desilets et al. , 2010). Although this approach almost removes the unrealistic effect of reduced hydrogen pools, the assumption of a single calibration paramter N 0 must be considered to be illegitimate due to significant biomass dynamics in the investigated period. The remaining deviation of the three calibration curves still indicates a small water reduction effect, however its magnitude is insignificant given the observational uncertainty of the neutron counter. It 10 remains an open questions whether a revision of the parameters of eq. 1 would better catch the local dynamics and further contribute to the interpretation of the signal.
Ponded water in local hollows, trenches, and the litter layer is not visible by the soil profiles of the monitoring network, which are typically installed in solid and elevated ground. In contrast, their effect can be visible in stronger oscillations and shift of the CRNS signal. The analysis demonstrates that the revised weighting of calibration data is essential to identify residual hydrological effects which otherwise can get lost by overfitting. By comparing CRNS data and point measurements, residual information could be used to identify additional processes like biomass dynamics or rainfall interception  (Baroni and Oswald, 2015). With the help of the methods presented here, we were able to identify those residuals to a much higher precision. As each compartment is distributed differently in the CRNS footprint, the contribution of each area is different and thus cannot be averaged adequately by a simple weighting approach.
In particular, the dynamical horizontal weighting has been applied here to demonstrate its capability to significantly improve CRNS performance. While existing data from point sensor networks could be re-weighted in post-processing mode, the question arises whether positioning schemes for upcoming soil moisture networks or calibration campaigns could adapt on the nature of neutron physics to maximize comparability. Obviously, it is impossible to provide a new general position plan, due to the temporal variability of W r and W d , and the heterogeneity of local structures and conditions. Instead, selection of sampling locations should depend on (1) their representativeness for local features, and (2) their distance to the sensor. In general, it can be strongly recommended to select about half of available sampling points within the nearest 25 m, since the conventional sampling scheme from  Franz et al.
Conclusions In this paper a general procedure for horizontal and vertical weighting of point measurements has been presented in order to calibrate and validate the CRNS soil moisture product. The method is based on revised spatial sensitivity functions (or weighting functions) from neutron physics simulations  (Köhli et al. , 2015). Noteworthy, the revised functions have been further advanced in the present work with an updated version of the neutron transport code URANOS, by adding dependency on air pressure and vegetation height, and by extending the analysis to distances below 0. 5 m. The performance of the conventional weighting functions has been compared with the revised functions using datasets from a variety of distinct sites located in Germany, the UK, and the US. The main conclusions are summarizes as follows: 1.
If the samples are arranged according to eq. 7, their equally weighted average would provide a value that is comparable to the CRNS product. On the other hand, if the footprint is covered by heterogeneous soil and landuse patterns, the sample locations should be adapted to distinct representative clusters, which in turn should then be weighted based on their areal contribution to the signal (see section 4. 4). 5. Data points in the first 0 to 10 m radius and 0 to 20 cm depth around the sensor are most important for calibration and validation purposes. It is thus recommended to reduce the uncertainty of those measurements, e. g. , by avoiding flints in the samples, or by increasing the number of samples in that area. 6. As previous studies have shown, the CRNS soil moisture signal could be calibrated to match the simple, equal average of the areal soil moisture in the footprint.
However, important hydrological features could be missed by doing so and data interpretation might become misleading. The revised weighting approach has the potential to reveal otherwise invisible hydrological features, like water in the biomass or litter layer, interception water storage, groundwater rise, as well as ponding in remote or local areas. The revised weighting functions presented here are provided in the supplementary material in R, MATLAB, and Excel (see Appendix C). Furthermore, an approximated weighting function W * r (Appendix B) has been suggested to simplify quick analysis of the horizontal contributions independently of the local wetness conditions. However, the latter approach should be taken with care, for its adequate performance has not been sufficiently confirmed in this work.
Within this study many datasets have been reanalyzed to test the revised weighting approach. Due to its overall success, it is recommended to revisit also other studies, especially where the conventional approaches have not led to the expected results (e. g. ,  Franz et al. , 2012a; Almeida et al. , 2014; Iwema et al. , 2015). In the light of the discussion provided, we recommend future studies to improve the sensor performance even further. For example by investigating the effect of different sampling designs and interpolation strategies, or by recalibrating the parameters of the theoretical line, N (θ). Specific URANOS simulations of the neutron distribution at the individual sites can further help to identify the contribution to the detector signal of different parts in the footprint. On the basis of the results gained by this study and in the light of the conclusions above, it can be deduced that CRNS stations placed in mostly homogeneous terrain offer the highest interpretability of its field-scale signal.
Tests with all datasets of this study have indicated that the corresponding soil moisture average deviates from the exactly-weighted average not more than 2 % v (not shown). However, the deviation highly depends on h and θ and thus can be an important source of error in temporal analysis where large ranges of humidity are expected. Also note that the integral of the approximated function does not scale with neutron intensity anymore, which has however no impact on normalized weights. Further studies will demonstrate whether eq. B1 is accurate enough to improve the CRNS performance under various wetness conditions and in different sites. If so, the reduction of computational effort will be valuable for regular analysis and for end users in the applied sector.
2 discusses the uncertainty related to a time series dataset in the Schäfertal catchment, where the footprint is only partly covered by monitored profiles. Section 4. 3 analyzes the potential of CRNS and validation datasets in Grosses Bruch and Wüstebach to identify additional hydrological processes. At the TWDEF site in section 4. 4, we use monitoring profiles in distinct parts of the footprint, which are individually weighted based on their areal contribution to the neutron signal.
Acknowledgements. MS acknowledges kind support by the  Helmholtz Impulse and Networking Fund through Helmholtz Interdisciplinary School for Environmental Research (HIGRADE). JI is funded by the  Queen's School of Engineering, University of Bristol ,  EPSRC , grant code:  EP/L504919/1. RR, JI and  Sheepdrove Organic Farm  activities are funded by the  Natural Environment Research Council  ( A MUltiscale Soil moistureEvapotranspiration Dynamics study (AMUSED ); grant number  NE/M003086/1 ). GB had financial support from the  Deutsche Forschungsgemeinschaft (DFG)  under  CI 26/13-1  in the framework of the research unit FOR 2131 " Data Assimilation for Improved Characterization of Fluxes across Compartmental Interfaces ". CR and MC acknowledge the support of this study by the  Integrated Carbon Observation System (ICOS)  infrastructure through the  Federal Ministry of Research and Education (BMBF).
Recruitment After 24 hours of mechanical ventilation, if the patient fulfils the inclusion criteria, the information letter will be given to his/her family. Cardiac Doppler ultrasound will then be carried out by an experienced doctor. The purpose of the ultrasound is to objectively evaluate the possibility of interpreting the measurements carried out and to collect baseline data for the patient. If the patient is echogenic, he/she will be included. The crosssectional surface area (CSA) of the left ventricular outflow tract and right atrial pressure will be measured at this time.
Expiratory tidal volume and respiratory rate Respiratory rate and tidal expiratory volume will be monitored by the ICU ventilator. Values will be recorded every 30 s and the mean will be calculated every 3 min. Power calculation and sample size CO will be measured using cardiac ultrasonography. There are few data on changes in CO during early rehabilitation in ICU. We chose to use existing data evaluated by cardiac ultrasound during a passive leg raise test. Based on the results of previous studies,  14  19 participants should be included to detect a difference in the mean CO of 1. 1 L between groups, and to reject the null hypothesis with a power of 90%. 14 The associated type I probability error is 0. 05. We plan to include 20 patients in total.
Statistical analysis Normally distributed data will be expressed as means (±SDs) and non-normally distributed data will be expressed as medians (and IQRs). A mean of three VTIs will be calculated every 3 min. Each image must contain at least three QRS complexes. The values of THb, HbO 2 and HHb will be collected continuously and means will be calculated every 3 min. Within-group data will be analysed using paired t-tests or Wilcoxon signed-rank tests. Two-way analysis of variance will be used to compare changes during the sessions between groups. A Bonferroni t-test will be used post hoc. The level of significance will be set at ≤0. 05.
Ethics and dissemination The study has been approved by our Institutional Review Board (Comité de Protection des Personnes Nord-Ouest 3). In conformity with the Declaration of Helsinki, all participants will be recruited voluntarily and they will sign an informed consent form. The results of the trial will be presented at national and international meetings and published in peer-reviewed journals. Results will be registered ClinicalTrials. gov. We will also disseminate the main results to all participants in a letter. The study has been registered with ClinicalTrials. gov (NCT02920684).
Acknowledgements The authors thank ADIR association for their support and  Johanna Robertson, PhD , medical translator, for editing the manuscript. Contributors CM, EF, TB, FEG, OC and BL are responsible for trial concept and design; CM, YC, GP, ARQ and BL are responsible for acquisition of data; CM, YC, EF, GP, TB, FEG, OC and BL are responsible for analysis and interpretation of data, drafting the article; all authors contributed to revising it critically for important intellectual content and final approval of the version to be published. CM had full access to all trial data and takes responsibility for the integrity of the data and the accuracy of the data analysis. Funding This work was supported by  ADIR Association.
Here we focus on the uncertainties due to the sampling and mapping methods, and summarize previous work in Section 2. These multiple sources of uncertainty make it difficult to estimate the uncertainties of observation-based ocean datasets, even for straightforward global variables such as ocean heat content. Furthermore, reported uncertainties are subjective in their dependence on cross-comparison of different observational strategies of a single model simulation or reanalysis product. In other words, from these methods of estimating uncertainty, there is no way to quantify how the statistics of the observed ocean differ from those of the true ocean, what is quantified is how strategies differ relative to one another. To more directly estimate the skill of an observing strategy, we propose a new EOSSE methodology: subject an ensemble of independent global ocean model outputs to a single observing strategy and measure the distribution of 'observed' ocean states and the corresponding distribution of the modeled 'true' ocean states.
Herein, metrics are recommended to be performed on the two distributions to quantify how 'observed' quantities differ from 'truth' as function of spatial and temporal scales, as well as by time and location. Methodological subjectivity persists in the choice of model and observing strategy, but the use of an EOSSE renders the method more powerful than either repeatedly sampling a single model output or inter-comparing different observation-based datasets. This methodology is described in Section 3. We demonstrate the method by quantifying the uncertainty of the observing strategy used in the creation of the In-Situ Analysis System 2013 (ISAS13) estimate of global ocean heat content (OHC) variability down to 700m between 1990 and 2013. The observing strategy will be applied to 37 independent model segments from the Community Climate System Model version 3.
CORA Hydrographic Profiles We used the quality-controlled global COriolis Ocean Dataset for Reanalysis (CORA) version 4. 0 dataset of subsurface ocean temperature and salinity profiles from January 1990 through December 2013  Cabanes et al. (2013). It contains most hydrographic profiles available in this time period, including those from Argo; XBT, CTD, and XCTD casts from research and commercial vessels; moored and drifting buoys; and CTD attached to marine mammals (see  Cabanes et al. (2013)  for more details). All measurements with a quality control flag other than "reliable" were discarded. Observations beyond the nominal range of the current observations were also discarded, which include data poleward of 60 o North and South  (Roemmich and Gilson (2009) ) and beyond a depth of 700m (the maximum depth of most pre-Argo observations).
ISAS13 Observing Strategy We use In-Situ Analysis System 2013 (ISAS13) observing strategy, which applies the In-Situ Analysis System tool verion 6 (ISAS-V6) on the CORA dataset by interpolating the CORA observations onto a standardized grid  (Gaillard et al. (2016) ). This objective analysis (OA) procedure uses the optimal interpolation technique presented by  Bretherton et al. (1976)  and depends on a mean reference (or first guess) field, a field of the expected variance about the mean field, and de-correlation length and time scales. The ISAS13 observing strategy employs this OA procedure using monthly means provided by the World Ocean Atlas 2005 (WOA05) monthly climatologies, monthly variances estimated from the observations relative to these climatologies, and decorrelation time scales chosen to represent 1) the resolution of the Argo network and 2) the first-mode Rossby radius of deformation on each point of their chosen analysis grid.
The first 341 years of model output are dropped to remove model spin-up effects, and the remaining 851 years of model output are separated into 37 23-year long segments; the same temporal span as the CORA database. The model has an overall drift of 0. 27 ZJ/yr, which is captured by the linear trend of the decomposition ∆Q ∆t. As a result different segments have different mean OHC, which is captured by Q M. The largest source of variability in OHC is the seasonal cycle. Figure 3 shows time series of Q -Q M and Q A for 10 of the 23-year segments. The seasonal cycle and the anomalies in Q are readily apparent. 4. 5 Application of the Observing Strategy to Model Data Synthetic observations are taken from each model segment by linearly interpolating the model data in latitude, longitude and time to the latitudes, longitudes, and times of the observations in the CORA dataset, and using the nearest-neighbor model 9 Ocean Sci.
Acknowledgements. This work was funded by  NSF   INSPIRE Award  # 1245944. We would like to acknowledge high-performance computing support from  Yellowstone  (ark: /85065/d7wd3xhc ) provided by  NCAR's Computational and Information Systems Laboratory , sponsored by the  National Science Foundation. We also thank the members of Ifremer responsible for maintaining the CORA data set for allowing us to use their optimal interpolation procedure, aiding in its implementation, and understanding the results. We also thank  Dibyendu Mandal  and the research groups of  Weiqing Han  and  Baylor Fox-Kemper  for participating in helpful discussions.