dataesr/scientific-paragraphs-categorization huggingface dataset:
The PICP values of the RMQS MIR S3M models remain around 90%, indicating that the magnitude of the effect of including SI data on the PI is related the performance of the spectral predictive model. Therefore, caution should be taken when using the uncertainty maps created from a DSM model based on a large proportion of SI data because the pre-defined PI of these maps can be probably underestimated. Since statistical models (including machine learning) always overestimate low values and underestimate high values, it will result in an SI data with smaller variability. When merging such an SI data with observations, the overall variability of merged data will decrease, leading to narrower PIW subsequently.
Limitations and way forward Our results clearly showed that the improvement of model performance was quite limited when the SI data had low accuracy. It is possible that integrating SI data with low accuracy would even decrease the performance of the DSM model. Therefore, further research is needed to investigate the effect of low-quality SI data in DSM modelling. It could be solved by determining the most likely accuracy threshold or criteria (e. g. R 2 ) for excluding the data from a low-quality spectral predictive model in DSM practices. These unknown samples beyond the validity domain (e. g. 95% confidence intervals in first two principal components of spectra) of a good spectral predictive model should also be avoided in DSM modelling  (Chen et al. , 2018b). The trade-off will be there to find the right way to incorporate more or less large uncertain datasets without reducing the prediction performance.
These large datasets may provide the advantage of better covering the geographical space, because they can be acquired at a low cost for a large range of locations, but they should not reduce the performance of the DSM model. It should be also noted that most of current spectroscopic techniques are still conducted in the laboratory condition that means we still need to collect soil data from field work (i. e. getting sampling staff into the field, collecting soil, bringing it back to the lab, air-drying, grinding and sieving). As a result, the reduction of cost from laboratory observations to SI data can be quite small when compared to the total cost of the whole procedure. The in-situ and on-the-go spectroscopic techniques can greatly reduce the cost as it avoids sample transportation and pretreatment  (Li et al.
Conclusions We have evaluated the value of additional SI data for mapping soil properties using DSM technique at a national scale. Based on two soil databases, namely, RMQS and LUCAS Soil, we summarized the main messages as below. (1) Complementing laboratory observations with SI data could improve the performance of DSM model when the ratio of SI data was greater than 50%, otherwise the improvement was negligible; (2) The magnitude of model improvement was greatly influenced by the proportion of SI data used in DSM model and by the accuracy of spectral predictive models; (3) A large part of laboratory observations could be replaced with cheaper SI data for DSM modelling when the spectral predictive model does not lose too much prediction precision; (4) Increasing the proportion of SI data in DSM model lowered the PICP and PIW, which may result in underestimation of prediction uncertainty for a given prediction interval, and thus caution was needed when using the uncertainty maps derived from such a DSM model for decision making;.
Acknowledgements This study is funded by  National Natural Science Foundation of China  (No. 42201054 ). RMQS soil sampling and physico-chemical analyses were supported by the  GIS Sol , which is a scientific group of interest on soils involving the  French Ministry for ecology and sustainable development , the  French Ministry of agriculture , the  French National institute for geographical and forest information (IGN) , the  French government agency  for environmental protection and energy management (ADEME), the  Institut de recherche pour le développement (IRD , which is a French public research organization dedicated to southern countries) and the  Institut national de recherche pour l'agriculture , l'alimentation et l'environnement (INRAE, which is a French public research institute) dedicated to agriculture, food and environment).
OSSE = Observation System Simulation Experiment. TRL = Technological Readiness Level. Licensed under CC BY-SA 4. 0 by Thomas Boniface (M) as described in this study, reporting (R) of the resulting data to a certified authority, and verification (V) by this authority, using data and models, that any deployment is successful at increasing CO 2 influx from the atmosphere and enhancing its sequestration in the ocean. Successful verification of removal and sequestration will result in certification of the mCDR. The last two MRV components are mentioned in the last section of the study. Our study for this "Frontiers in Ocean Observing" supplement of Oceanography focuses on the observational aspects of the monitoring of open-ocean mCDR deployments, with less emphasis on the corresponding, essential modeling components.
Of course, control regions will eventually become "contaminated" by the spread of DIC-via ocean circulation-from mCDR deployed elsewhere (e. g. ,  Boyd and Bressac, 2016) , but they could be used for model validation before this occurs and even after. Failure to implement either pre-deployment periods or control regions would make attribution impossible and therefore compromise the monitoring of all mCDR deployments in a given ocean region. In addition, an open registry or metadatabase of the mCDR pilot studies and deployments would be very useful in this context. It would provide information, in particular for modelers, on the location and depth of each activity and key information on its technical aspects (e. g. , for alkalinization, the mineral type, timing, and amount of alkalinity added).
GENERAL REQUIREMENTS FOR AN mCDR MONITORING SYSTEM Guidelines for monitoring were set for the Global Ocean Observing system (GOOS) in the context of the Framework for Ocean Observation (FOO;  Tanhua et al. , 2019). A key element of the FOO is its organization and coordination around essential ocean variables (EOVs) rather than specific observing systems. The mCDR operational system described here differs from the GOOS observations, but also focuses on EOVs. We advocate that "essential mCDR variables" would include most current EOVs (see GOOS, 2021), along with more detailed data on lower atmosphere CO 2 concentration and oceanic DIC used to estimate air-sea CO 2 flux. Variables would also include wind speed, which has a strong influence on air-sea gas exchange. The effects of mCDR deployments on carbon capture and sequestration will accumulate over time.
To do so, at least two parameters of the carbonate system should be measured in the water column. These parameters include pH, total DIC, total alkalinity (TA), and CO 2 partial pressure (pCO 2 ). Detecting changes in the carbonate system is challenging, and detecting a superimposed mCDR effect would be very difficult. The monitoring system would combine satellite remote sensing (Figure  2 ) and long-term regional in situ measurements. The latter would be performed with autonomous robots (see Box 1), as described by  Chai et al. (2020). Biogeochemical data would subsequently be analyzed using AI and assimilated in models. The integration of these platforms, analyzing their data with AI, and combining the data with models is already partly implemented in open-ocean research (e. g. ,  Claustre et al.
, 2021)  and could be readily applicable to monitor open-ocean mCDR deployments. Ideally, the observational and modeling components of the mCDR monitoring system should be in place prior to an mCDR deployment. If this is impossible, data collected by the global networks of Biogeochemical-Argo (BGC-Argo) floats (Figure  3 ) and ocean color satellites (Figure  2 ) could document natural variability and contribute to the validation/calibration of models required for attribution.
GOVERNANCE OF AN mCDR MONITORING SYSTEM Published mCDR studies have mostly considered research aspects for future deployments as explained in the Introduction. Our study represents a departure from these studies as it details an operational system designed to monitor large-scale open-ocean mCDR deployments. For verification to be credible, the monitoring system and the interpretation of measurements in terms of sequestration efficiency must be performed independently of the entities deploying mCDRs. In addition, all monitoring data should promote the FAIR principles of findability, accessibility, interoperability, and reuse. Meeting these operational characteristics will require governance and best practices fundamentally different from those of curiosity-driven or targeted research (Chapter 9 of NASEM, 2022).
First-order removal rate coefficients (k) are used in predictive equations for estimating effluent concentrations from horizontal flow (HF) wetlands. Due to limited resources, influent and effluent concentration data from existing systems are frequently used in the estimation of k values from operating systems, but another choice is to use concentration data along the longitudinal profile of the HF wetland. Based on a dataset with 41 HF wetlands/studies obtained from a literature survey, with chemical oxygen demand (COD) measurements at different sampling points, volumetric (k V ) and areal (k A ) removal rate coefficients for the Tanks-In-Series (TIS) model have been obtained using the two estimation methods. In general, removal rate coefficients derived from longitudinal profiles of concentrations were higher than those obtained by using data from influent and effluent concentrations, reflecting the fact that constituent removal is mostly accomplished before the wastewater reaches the outlet zone.
Recognizing the importance of this statement made by  Kadlec & Wallace (2009)  in terms of deriving k coefficients that may be useful for the design of new HF systems, this paper aims at investigating it in more detail, and with more experimental evidence. Kadlec and Wallace's textbook resorts to a very limited number of references in this particular topic, and most of them are from free water surface wetlands, which have a different hydraulic behavior compared with subsurface flow horizontal wetlands. Therefore, the objective here is to compare both estimation methods (influent/effluent concentrations vs. longitudinal profiles of concentrations) based on experimental evidence from a dataset of several operating HF wetlands in which longitudinal profiles of concentrations were available, together with influent and effluent concentrations.
This comparison of the two estimation methods is not intended to be a mere academic exercise. On a practical level, the relevance of estimating removal rate coefficients in HF wetlands lies in the intended application: (i) generation of k values to be used for the design of future wetlands and (ii) derivation of k values that allow an increase in the understanding of the internal behavior of existing wetlands. These two objectives are discussed in this paper, in syntony with the interpretation of the results obtained with the two estimation methods applied to the dataset of existing wetlands.
MATERIALS AND METHODS This study used information available in a dataset originally compiled by  Soares (2016)  and  Ferreira et al. (2020) , based on a literature survey of studies that had obtained data on the longitudinal profile of concentrations inside HF wetlands. A total of 41 different wetlands and/or operating conditions have been compiled, and the constituent analyzed was chemical oxygen demand (COD). The dataset, adapted for this study, is presented in the Supplementary Material, and is based on the following references:  Valentim (2003) ,  Brasil (2005)   (2013). Each study adopted different strategies for sampling frequency and duration of monitoring, and the values of flowrates and influent and effluent concentrations used here for each wetland/study are based on averages from periods of records, as presented by the authors of the publications.
From the 41 units/studies investigated, 24. 4% received raw influent, 68. 3% received primary effluent, and 7. 3% received secondary effluent. The descriptive statistics of the main variables are presented in Table  1. From the table, it can be seen that the HF wetlands used here are mainly at pilot-or small-scale, and the interpretation of the results should take this into account. However, even with this limitation, the dataset is still considered important because, besides providing the internal longitudinal profiles of concentrations, it also includes the physical dimensions of the units (not  always included in many publications in this field), the hydraulic retention time and applied areal hydraulic loading rate, besides other information. Area A (m 2 ) Length L (m) Width W (m) Liquid depth h (m) Aspect ratio (L/W) Ratio L/h Flow Q (m 3 /d) HRT (d) Areal HLR (m 3 / m 2 •d) Areal OLR (gCOD/ m 2 •d) Influent COD C in (mg/L) Effluent COD C out (mg/L) There was no tracer data with information on the number of apparent TIS (N) in each wetland.
This topic will be detailed further along in the paper. Also from Table  2 , it is noteworthy to mention that the median areal k A value for COD (0. 081 m/d), obtained from influent/ effluent concentrations in this dataset, is comparable to the median value obtained by von Sperling et al. (2022b) for BOD (0. 066 m/d, also using influent/effluent data and a completely different dataset). This latter value is based on a separate independent source, comprising an extensive database utilized by  Kadlec & Wallace (2009)  in their book. The Kadlec and Wallace dataset adopted the P-k-C* approach, which was further converted to the TIS model by von  Sperling et al. (2022b). As mentioned in the Materials and Methods section, the k values reported in Table  2  are not based on the standard liquid temperature of 20 °C, because this information was not available in the sources consulted.
This dataset of 41 wetlands/studies is predominantly from warm-climate countries, but the influence of temperature in k values is believed not to be high for the specific case of organic matter, as suggested by  Kadlec & Wallace (2009). These authors, working with a large dataset of HF wetlands, using the P-k-C* approach, found the modified-Arrhenius temperature coefficient (θ) for BOD removal to be close to 1. 0, varying between a typical range of 0. 96 and 1. 02 (30th and 70th percentiles). Indeed,  Dotro et al. (2017)  suggest that for design purposes of new HF wetlands, θ could be adopted as 1. 0 in the case of BOD. This reasoning of minimizing the influence of temperature was also adopted for COD, which is the constituent analyzed in this research. Regarding the derivation of k values based on the longitudinal profile of concentrations, which are based on the minimization of an error function, the overall fitting between observed and estimated concentrations along the longitudinal distance were very good.
However, it can also be seen that, with this dataset, k V values lower than 0. 6 d À1 were approximately the same with both methods. Figure  3  (right) presents a similar comparison, made for the areal-based k A coefficient. Also here, with the exception of the range with low k A values, in general, the values obtained with the longitudinal profile method are higher than those derived from influent/effluent concentrations. This endorses the statement made by  Kadlec & Wallace (2009) , the verification of which was one of the main drivers of this study. In order to understand this a bit further, two actual profiles taken from the 41-wetlands database are shown in Figure  4 , aiming at representing two typical situations. The figures plot measured values of COD along the length of two different horizontal wetlands, together with calculated longitudinal profiles using Equation (  5 ) and k V coefficients obtained employing the two different approaches (measurements of only influent/effluent concentrations and measurements made in different points along the longitudinal axis of the wetland).
This method is associated with a more faithful representation of the internal decrease of the concentration of the constituent under analysis. If the number of studies of concentration profiles increases, allowing the construction of a solid database of k values based in internal profiles available in the literature, supported by a large number of HF wetlands with different operating conditions, then this method could start to be the preferred choice. In this case, the designer would decide on how conservative he/she wants to be, based on a better-informed decision procedure. Designs for effluent concentrations close to C* For a designer trying to achieve effluent values close to background concentrations (C*), the two approaches diverge widely. Using the influent/effluent approach, a designer would calculate that a wetland area close to infinity would be required, which is not realistic.
CONCLUSIONS AND FINAL REMARKS Based on a database with 41 HF wetlands/studies obtained from the literature, with COD measurements at different sampling points, volumetric (k V ) and areal-based (k A ) first-order removal rate coefficients for the TIS model have been obtained using two estimation methods associated with different monitoring procedures: (i) influent and effluent concentrations and (ii) longitudinal profiles of concentrations. It was confirmed that, in general, k coefficients derived from the longitudinal profile of concentrations method tend to be higher than those obtained by using data from only influent and effluent concentrations, indicating that constituent removals may be mostly accomplished before the wastewater reaches the outlet zone. The median values and typical ranges of areal-based removal rate coefficients (k A ) obtained in this study using the influent and effluent concentrations method are comparable to those cited in the literature for organic matter, also based on the same estimation procedure, and representing an independent and larger dataset.
Deriving coefficients from longitudinal profiles is more comprehensive, because it gives a better explanation of the internal removal taking place in the wetland, and so it is expected to be a better descriptor of HF wetlands performance. However, literature is scarce on the availability of k coefficients using longitudinal profiles. The simple and widely used approach of calculating k V and k A from influent/effluent values may lead to a more conservative and safer design of HF wetlands, because the resulting values of the removal rate coefficients are usually lower than those obtained from longitudinal profiles of concentrations. Besides that, most of the k coefficients obtained and reported by researchers are based on this simplified influent/effluent approach. Although the dataset used here is based mostly on pilot-and small-scale HF wetlands, the results obtained are conceptually coherent and allow an expansion of the understanding of removal rate coefficients in HF wetlands, together with their interpretation, applicability, and limitations.
Two reanalysis datasets have been used to calculate climate variables anomalies: Rutgers University Global Snow laboratory (hereafter "GSL-NASA") for snow cover (SNC)  (Brown and Robinson 2011)  spanning from January 1 st , 1967 to March 31 st , 2012 and ERA-Interim  (Dee et al. 2011 ) for all the other nine variables in the period 1979-2012. The European gridded observational dataset E-OBS used daily mean surface air temperature (TAS) spanning from 1950 to 2005  (Haylock et al. 2008) , to determine days corresponding to CW useful to compute composite anomalies during and before CW of the ten above-mentioned variables (see below Calculation of composite anomalies). For each variable, the maximum number of CMIP5 models available for the historical period  are used (between 17 and 35 models depending on the variables, see Table  1 ).
Snow cover data Land snow cover is one of the slowly varying components of the climate system. Its relevance in winter is based on its insulating and reflecting properties. As European ACCESS1-0 CSIRO-BOM, Australia × × × 48 ACCESS1-3 " × × × 43 bcc-csm1-1 BCC, China × × × × × × × × × × 80 bcc-csm1-1-m " × × × × × × × × × × 61 BNU-ESM G CESS, China × × × × × × × × × × 59 CanESM2 C CCMA, Canada × × × × × × × × × × 62 CESM1-BGC N SF-DOE-NCAR × × 63 CESM1-FASTCHEM " × × 59 CMCC-CESM CMCC, Italia × × × × × × × × × 61 CMCC-CM " × × × × × × × × × 59 CMCC-CMS " × × × × × 70 CNRM-CM5 CNRM, France × × × × × × × × × × 69 CSIRO-Mk3-6-0 C SIRO-QCCCE, Australia × × × × × × × × × × 53 CCSM4 N CAR, USA × × × 61 EC-EARTH ICHEC × × × 54 FGOALS-g2 LASG-IAP, China × × × × × × × × × × × 57 FGOALS-s2 L ASG-IAP, China × × × × × 79 GISS-E2-R N ASA-GISS, USA × × × × 79 GFDL-CM3 NOAA GFDL, USA × × × × × × × × × × 58 GFDL-ESM2G " × × × × × × × × × × 67 GFDL-ESM2M " × × × × × × × 59 HadCM3 MOHC, England × × × × × 63 HadGEM2-CC " × × × × × × × × × × 49 HadGEM2-ES MOHC/INPE × × × × × × × × × 42 inmcm4 INM, Russia × × × × × × × × × 54 IPSL-CM5A-LR IPSL, France × 68 IPSL-CM5A-MR " × × × × × × × × × 66 IPSL-CM5B-LR " × × × × × × × × × 57 MIROC4h M IROC, Japan × × × × × × × × × × 41 MIROC5 " × × × × × × × × × × × 48 MIROC-ESM " × × × × × × 49 MIROC-ESM-CHEM " × × × × × × × × 57 MPI-ESM-LR MPI-M, Germany × × × × × × × × × 56 MPI-ESM-MR " × × × × × × × × × 53 MPI-ESM-P " × × × × × × × 61 MRI-CGCM3 M RI, Japan × × × × × × × × × 64 MRI-ESM1 " × × × × × × × × × 68 E-OBS - × 41 GSL-NASA - × - ERA-Interim - × × × × × × × × × - intra-seasonal cold events are considered in this study, both daily and European scales for snow cover are needed, which is not so common for snow cover datasets.
The value of this threshold at local level is shown in Supplementary Figure  S1a , going from 8 °C in Southwestern Europe to -20 °C in Northeastern Europe, with very few changes across reanalysis datasets (Supplementary Figure  S1b ). A similar index was already used in several studies  (Kolstad and Bracegirdle 2008; Cattiaux et al. 2013; Twardosz and Kossowska-Cezak 2016). Hence, for each dataset, we obtain a catalog of CW and extreme cold days corresponding to these CW, composed of occurrence and temperature anomalies over the 1950-2005 period. This definition allows studying the CMIP5 models or E-OBS dataset variability in their own climate (i. e. with respect to their wintertime climatology and standard deviation). Note that with this definition, almost all cold waves of very heavy to medium intensity defined by Meteo-France are captured in the period 1950-2005 of E-OBS.
1a-c. This European bias in CWN could be related to the inadequate representation of physical processes during or before CW. The analysis below through the assessment of 10 climate variables in models and reanalyses aims to clarify, among others, the existing biases of associated variables to CW. Figures 2, 3, 4, 5, 6, 7  and 8 present the results of composite anomalies for CMIP5 models in Europe and reanalysis datasets for associated variables (SNC, SNW, RLDS, RSDS, CLT, SST and ZG) during CW events. Supplementary Figure  S4  shows the comparison of the spatial patterns between CMIP5 ensemble mean (left part) and reanalyses (right part) for each variable (sorted by row) before the CW. Snow cover during CW is anomalously positive with an average difference of about ~ 12. 0% during CW mainly located in Western Europe both for models and reanalysis (Fig.