dataesr/scientific-paragraphs-categorization huggingface dataset:
The Reference Simulation In this section, a validation of the reference simulation is presented using observations mentioned in Section 2. 1. An analysis of the PM component of this simulation is presented. Finally, a comparison is performed between the simulations including direct/indirect aerosol effects and the corresponding reference cases. The location of stations are shown in Figure  1. The statistics and the figures presented in the following section were produced with Evaltools, a python package providing different tools for model/observation comparisons. The metrics provided in Tables  2  and 3  are explained in the documentation of the Evaltools package  [49]. More information about how these metrics are calculated can be observed in  [50].
Meteorological Variables Table  2  shows the error statistics for daily minimum, maximum and average temperature, average wind speed, precipitation and relative humidity with the E-OBS dataset. For the three domains, the model reproduced the evolution of the temperature (maximum and average values) well; however, an underestimation of the daily minimum value is observed. Wind speed is also well represented in the two coarser domains; however, in the finest domain only one station with a complete set of data was found (not presented here because of the statistic sample being too small). Relative humidity is also well reproduced by the model. It should be mentioned that the representation of common stations between the three domains is quite similar, meaning that a higher resolution does not automatically mean better representation of meteorological conditions, at least for this period of the year and for this region.
Meteorological data from Météo-France and those from the SIRTA observatory  [51]  were also added. In total, eight stations in the Parisian area were used for these comparisons (Figure  1 ). The representation of these four meteorological variables is quite correctly conducted by the model for all three domains similar to the E-OBS dataset. These comparisons show (Figure  2 , (a):(d)) that the resolution seems not to be a key issue for the accuracy of the meteorological variables. In all cases, the correlation is slightly improved when the resolution increases. Since a spectral nudging is activated, the weak differences between the domains is expected since the nested domains remain influenced by the large scale patterns, which include observations. Nudging is efficient to avoid divergences of meteorological simulations; however, at a local scale an improvement is expected with much more detailed processes influencing the simulation.
PM Components In addition, nitrate (NO - 3 ), ammonium (NH + 4 ) and total organic aerosols (OA) were compared to data obtained from the EBAS database presented in Figure  2h-j. Sulfates (SO 2- 4 ) and black carbon (BC) are not presented since no complete and reliable data were found for any of the domains for these species. It should also be noted that only stations having hourly data were used for these comparisons; stations having daily data were filtered out. In the end, four and one stations were used for the coarse and the intermediate domain, respectively. The results demonstrate a good correlation between the measurements and the observations; the episode is simulated at the right time by the model and the intensity of the simulation matches the observations as well, especially for the coarse domain.
Ozone Vertical Profile The evaluation with ozone sondes data described in Section 2. 1 is presented here. These data aim to validate the vertical simulation of pressure, temperature and ozone concentrations. The number of available stations for these comparisons are limited; in fact, there are no stations in the finest domain with usable data for our simulation period. Therefore, only the performance of the model for the coarse FAIR30 and the intermediate PAR10 domains are presented here. There are 11 stations with available data for the simulation period on the coarse domain, and only one station has a consistent dataset for the intermediate domain (Figure  1 ). The results of these comparisons are shown in Figure  4 , which displays an averaged profile for the 11 stations on the coarse domain and the one station on the intermediate domain.
Discussion Several anthropogenic emission reduction scenarios have been performed using the WRF-CHIMERE modeling system. The simulations focused on an air pollution episode in February 2015 with high PM concentrations recorded over the Paris region exceeding the 2021 WHO guidelines. The reference simulation has been conducted for the entire month of February for validation purposes, but the anthropogenic emission reduction scenarios are performed only for the episode period, from the 10 to 17 of February 2015. A thorough validation was performed to assess the performance of the model (Section 3. 1). The evaluation shows that the reference case performs well when compared to surface data for meteorological variables and concentrations. The comparisons show good performances for the meteorological parameters (temperature, wind speed and direction, and relative humidity) for the three domains.
Acknowledgments: This work was granted access to the HPC resources of the Irene supercomputer under the allocation for the project  gen10274  made by GENCI. We acknowledge the E-OBS dataset from the EU-FP6 project UERRA (http://www. uerra. eu (accessed on 10 January 2023)) and the Copernicus Climate Change Service, and the data providers in the ECA&D project (https://www. ecad. eu (accessed on 10 January 2023)).
, 2022; von Chamier et al. , 2021). Therefore, there is a necessity for open-source programs that include a complete pipeline for processing large microscopy biological datasets and that can be employed across different multidisciplinary biological studies by providing a flexible and modular structure. Through CellWalker, we aim to provide a set of tools that include both segmentation and morphological analysis in the form of a pipeline that contains independent modules dedicated for various tasks. We utilize existing open-source technologies to make CellWalker visible-source wherever possible. This approach allows users to use customized input data, tweak the algorithms and employ the in-built tools in the software packages inside which the CellWalker modules run.
The hypothesis that climate exerts a first-order control on landscape form and function through physical and chemical erosion dates back more than a century 1. Nonetheless, quantifying the influence of climate on erosion rate and morphology in any given landscape has proven difficult. Whereas numerical models provide clear rationales for how precipitation foci and oscillations may impact deformation and sediment-flux regimes in orogenic systems  [2] [3] [4] [5]  , empirical evidence has remained equivocal with convincing evidence both for and against strong coupling between climate, erosion, and tectonics 6. This ambiguity partially derives from temporal and spatial mismatches in both the datasets and proxies used to infer such linkages. For example, many interpretations rely on comparing present-day precipitation patterns with erosion and/or tectonic rates measured over 10 2 -to 10 6year timescales.
2  and Supplementary Dataset 1). b Calculated sediment-accumulation rates for the Río Iruya section  32  (mean in black and maximum and minimum envelope in gray). Dashed red line is the overall mean accumulation rate for the section. Rate uncertainties are greatest for brief magnetochrons where the precise positions of polarity boundaries are not well resolved relative to the length of the magnetochron (see Supplementary Dataset 1). Mean exhumation rates for cosmogenic radionuclide samples (blue dots and trend line) are based on the sample depth below the pre-diversion river level and assuming 150 years since the diversion. The associated error envelope assumes a+/-15 m error on the reconstructed prediversion surface elevation. c An error-weighted two-nuclide test for complex transport and burial history showing minimal diversion from the expected decaycorrected  26  Al:  10  Be ratio (7:1 in dashed blue line) for all  26  Al samples with detectable concentrations and AMS errors less than 35%, minus the outlier at 2.
CLIMBER-2 Climate Model Results from the CLIMBER-2 model suggest that, although precipitation oscillations in the region are dominated by the 23-kyr precession and 41-kyr obliquity-frequency bands during this period (Fig. 7 ), 400kyr eccentricity cycles strongly modulate changes in amplitude of the 23-kyr precipitation cycles (Fig. 4e, f ). When eccentricity is high, for example, precession cycles cause ~20% swings in summer insolation at 23°S, cycles that are damped to ~2% during periods of low eccentricity (Fig. 4b ). These model results are notable because paleo-erosion rates correlate with the amplitude of CLIMBER-2 precipitation oscillations from ~4. 0 to 2. 4 Ma (Fig. 6 ), which themselves are largely driven by insolation. Although spatially coarse, several lines of evidence suggest that the CLIMBER-2 data are, broadly, a reasonable predictor of Plio-Pleistocene climatic variability.
1b )? Recognizing these questions, our preferred hypothesis to explain the observed 400-kyr erosion-rate pattern calls upon nonlinear threshold hillslope behavior driven by landsliding coupled to channel incision (e. g. , lateral or vertical)  55, 56  , causing erosion rates to nonlinearly track precipitation amplitudes above some threshold (Fig. 8 )  57. During periods of increased aridity, sediment production and Fig. 5  | 10 Be-derived paleo-erosion rates from the Río Iruya and nearby Quebrada de Humahuaca from 6 Ma to 1. 8 Ma. a Paleo-erosion rates calculated for the Río Iruya catchment based on the preferred uplift regime and mean catchment production rate evolution scenario (see Supplementary Fig. 4b  and Supplementary Dataset 2). Black circles indicate samples, and the blue piecewise cubic spline is interpolated between sample errors (±2σ envelope).
V-SMOW -Vienna standard mean ocean water. Erosion rates compared to a, b 400-kyr eccentricity, c, d insolation at 23°S latitude, and e, f the CLIMBER-2 precipitation dataset for three different time periods: 1. 8-2. 48 Ma (red), 2. 49-3. 8 Ma (orange), and 3. 8-6 Ma (or 5 Ma for the CLIMBER-2 dataset) (blue). Pearson's correlation coefficients and p-values are presented for three periods of time below each plot with 95% confidence interval envelopes shown for each correlation. Note the strong correlation and low p-values for the relationships between erosion rate and 400-kyr eccentricity and CLIMBER-2 precipitation for the period from 2. 49-3. 8 Ma (orange) in a and e. Also note the different x-axis scalings between the upper and lower panels. transport would diminish significantly, thereby allowing long-timescale, slower hillslope diffusion processes to dominate the erosion-rate signal.
Empirical results from nearby paleoerosion rate studies  13, 19  have proposed that catchment architecture and transport distance may dictate which periodicities are preserved in the sediment record, whereby preserved periodicities are proportional to the square of the river length above the site of deposition  19. Whereas this regional observation is compelling, the Río Iruya dataset represents an integrated geochemical signature of catchment erosion that is largely independent from the autogenic geomorphic processes that drive fluvial terrace or alluvial fan preservation. Evidence from experimental studies, however, indicates that catchments undergoing pronounced aggradation/incision cycles, and when coupled to significant long-term sediment storage, may result in mixing temporally disparate erosion-rate signals during periods of incision as modern 10 Be signals are progressively mixed with stratigraphically older, cosmogenically-decayed signals  66.
8 ). Another interesting feature of the paleo-erosion rate dataset is the overall increase in erosion rate and loss of the 400-kyr cyclicity starting at ~2. 4 Ma (Fig. 5a ). One possible explanation is that the loss of 400 kyr erosion-rate cyclicity may have been driven by a watershed reorganization in the headwaters of the Río Iruya associated with the ~2. 4 Ma provenance shift. Alternatively, the loss of erosion-rate cyclicity may have resulted from a change in precipitation conditions associated with the continued growth of the Eastern Cordillera, eventually concentrating precipitation along the eastern range front as the orographic barrier became more pronounced. This explanation is supported during this time by an order-of-magnitude decrease in erosion rates on the leeward side of the growing Eastern Cordillera in the Quebrada de Humahuaca 21 (Fig.
Sample age model and uncertainty Detailed timing in the Río Iruya section was established using a densely sampled magnetostratigraphic transect combined with several precisely dated, intercalated volcanic ash beds yielding robust age control in the section that spans 1. 94 to 6. 49 Ma 32 (Supplementary Fig. 2  and Supplementary Dataset 1). Calculation of age uncertainties on paleoerosion rate samples was performed using published magnetostratigraphic data and allowed the polarity reversal boundaries to be flexible with respect to their confining samples or radiometric ash ages. Assuming a linear accumulation rate between polarity reversals, the maximum and minimum age of each cosmogenic radionuclide sample was determined by moving the reversal boundaries as far forward (making the sample older) and as far back (making the sample younger) as possible in the stratigraphy (Supplementary Fig.
8 ). This method allows for proper quantification of sample-age uncertainty and highlights the asymmetric nature of age uncertainty associated with unevenly spaced sampling and polarity reversal boundaries. Midpoint, maximum, and minimum age uncertainties for each sample are reported in Supplementary Dataset 2, with a median maximum and minimum divergence from the midpoint age of 0. 033 Myr (n = 41) and 0. 034 Myr (n = 39), respectively. For samples in the lower part of the section defined only by ashes (5. 5-6. 49 Ma), the midpoint age was calculated using a linear interpolation based on stratigraphic location between the ashes. Uncertainties for these samples were cautiously set as the 75th percentile of maximum and minimum uncertainties from the magnetostratigraphy component of the dataset (0.
During digestion of the quartz samples, a low-ratio  9  Be spike with a 10 Be/  9  Be ratio value of 3 × 10 -15 was added. In addition, during each digestion batch an empty teflon beaker was treated with approximately the same maximum volume of hydrofluoric acid as used in that given batch of samples and was spiked with the same low-ratio  9  Be spike. These blanks were then processed through the same protocols as samples, yielding  10  Be/  9  Be values for the 5 blanks ranging from 3. 5 to 13 × 10 -15 (Supplementary Dataset 2 and 3). No  27  Al spike was added, given the high concentrations of Al in the quartz mineral grain lattice, with no 26 Al contamination detected in the processed blanks during the chemical processing steps. 10 Be and  26  Al targets were then measured via accelerator mass spectrometry at the Purdue Rare Isotope Measurement Laboratory (PRIME) using the 07KNSTD standardization and assuming a 10 Be half-life of 1.
Because P 0 varies with latitude, altitude, and topographic shielding, modern mean effective production rates (topographic shielding factor x production rate) across the Río Iruya watershed were estimated using the 90-meter Shuttle Radar Topography Mission digital elevation model and the CRONUScalc Matlab codes  75  assuming a sea-level high-latitude (SLHL) production rate of 4. 01 atoms g -1 yr -1 and based on the time-independent Lal-Stone scaling (St)  31, 76, 77. Assuming rapid transport of sediments from the hillslopes into the fluvial network and proper amalgamation in the fluvial system of an integrated catchment signal, the concentration of 10 Be in a river sand sample can be used to calculate the erosion rate, R E , of the upstream catchment  24  where R E = Λ ρ P 0 N E À λ ð2Þ and λ is the radioactive decay constant for 10 Be (yr -1 ) and N E is the  10  Be concentration of a modern river sample (atoms g -1 ).
Production from spallation and fast and slow muons is integrated throughout both the burial and exhumation terms (cf. Supplementary Table  1 ), though production during exhumation in this study was negligible due to the rapid incision of the canyon. Each component and associated uncertainty were constrained and inverted progressively back through time (N X to N E ) utilizing a Monte Carlo approach (100,000 iterations) to compound uncertainty in the erosionrate component (N E ) and calculated erosion rate (R E ) for each sample based on Eq. 2 (Supplementary Dataset 2 and 3 and Supplementary Table  1 ). Resulting asymmetric uncertainty distributions derive from the exponential depth dependence of cosmogenic radionuclide production, the time dependence of decay, and asymmetry in age uncertainties (Supplementary Fig.
8 ). Whereas uncertainties were not assigned to production rate values in the source area (P E ) and foreland depocenter (P B ), several scenarios of production-rate evolution through time were modeled to account for a wide range of possible catchment uplift scenarios (see Supplementary Fig. 4  and Supplementary Dataset 2). These differing scenarios, it should be noted, only result in minor changes to the absolute magnitude of calculated paleo-erosion rates from 1. 8 Ma to 4 Ma and not the observed patterns or cyclicity. The transport contribution (N T ) to measured cosmogenic radionuclide concentrations in the analysis is assumed to be negligible based on: (1) the close proximity of the depositional basin to a relatively steep source area starting at ~4 Ma;  32,44 and (2)  good agreement between  26  Al:  10  Be sample ratios (for  26  Al AMS errors <35%) with expected  26  Al:  10  Be ratios assuming no significant burial during transport from ~2-3 Ma (Fig.
Here we implemented version 2. 3 of CLIMBER-2 that includes atmospheric, ocean, and terrestrial components, including a dynamic vegetation model and thermodynamic sea-ice model  50, 79. The CLIMBER-2 atmospheric component is represented by a 2. 5-dimensional statistical-dynamical model with latitudinal resolution of 10°and longitudinal resolution of ~51°(7 zones) using a daily time step. Circulation, temperature, and humidity are calculated across 10 vertical levels in the model and 16 levels for long-wave radiation. Vegetation potential in each grid cell is recorded at a yearly time-step based on temperature and precipitation using the terrestrial model VECODE  81. The ocean model integrates the Pacific, Atlantic, and Indian oceans via connection through the Southern Ocean and calculates zonally averaged flow between grid cells, as well as temperature and salinity changes, at a time step of 5 days, a latitudinal resolution of 2.
Frequency analysis was performed using AnalySeries software version 2. 0. 8. Both precession and long-eccentricity frequency filtering components were acquired using Gaussian shaped filters at 0. 044 and 0. 0025 with a bandwidth of 0. 008 and 0. 001, respectively. Wavelet analyses were performed using previously described methods  85. Due to the simple spatial representation of landmass in the CLIMBER-2 model, a geographic offset was accounted for in assessing data for South America (e. g. , Fig. 1b ) as the continent is shifted westward by ~35-45 degrees of longitude in the model (see Supplementary Fig. 5 ). CLIMBER-2 outputs used in this paper are provided in Supplementary Dataset 4.
Acknowledgements This work was supported by  NSF  grant  EAR-1148233  to W. H. A and D. W. B and a Jackson Distinguished Postdoctoral Fellowship to G. B. F at the  University of Texas at Austin. B. d. B was funded by  NWO Earth and Life Sciences (ALW) (# 863. 15. 019 ) while the CLIMBER-2 dataset has been generated by E. T. within the framework of the  NWO-ALW  funded  Vici  project (# 865. 10. 001 ) of L. J. L. We thank  Louise McCarren  for assistance in the field and  Taylor Schildgen  and  Heiko Pingel  for invaluable discussions regarding the manuscript.
In this paper we present a new reactive transport code for the efficient simulation of groundwater quality problems. The new code couples the two previously existing tools OpenFoam and PhreeqcRM. The major objective of the development was to transfer and expand the capabilities of the MODFLOW/MT3DMS-family of codes, especially their outstanding ability to suppress numerical dispersion, to a versatile and computationally efficient code for unstructured grids. Owing to the numerous, previously existing transport solvers contained in OpenFoam, the newly developed code achieves this objective and provides a solid basis for future expansions of the code capabilities. The flexibility of the OpenFoam framework is illustrated by the addition of diffusional processes for gaseous compounds in the unsaturated zone and the advection of gases (multiphase transport).
2012 ) and employed the USGS code PHREEQC as reaction simulator (e. g. ,  Appelo and Willemsen 1987; Prommer et al. 2003; Parkhurst et al. 2010; De Sousa 2012; Healy et al. 2018; Muniruzzaman and Rolle 2019; Lu et al. 2022). Indeed, the popularity of coupling PHREEQC to flow/solute transport simulators has triggered the USGS to develop and release the PhreeqcRM API (available under GNU license), aimed at simplifying the mechanics of model coupling  (Parkhurst and Wissmeier 2015). The reasons for the popularity of using PHREEQC as reaction module for the computation of geochemical processes are manifold: 1 it is distributed with a variety of thoroughly reviewed and validated thermodynamic databases; 2 it has a built-in BASIC interpreter, allowing for a highly flexible definition of complex reaction rate laws and output post-processing options; and 3 the ability to handle calculations involving solution density, rock volume, and partially saturated porous medium.
Clearly, there remains a strong need for an efficient PHREEQC-based reactive transport simulator that can simultaneously overcome the apparent disadvantages of structured grids while achieving an accurate solution of the solute transport problem under challenging conditions. This paper reports the development and main features of muFlowReacT (muRT, freely available under the GNU license), a code which (1) significantly expands the capabilities of OpenFoam toward a versatile groundwater flow and solute transport simulator and (2) couples PhreeqcRM to OpenFoam to allow for the simultaneous simulation of a wide range of (bio)geochemical processes that can affect groundwater quality. In this paper, we first present the formulation of the equations used in the OpenFoam framework, and the methods for treating boundary conditions, followed by the presentation of the employed coupling strategy between OpenFoam and PhreeqcRM.
Overview OpenFoam is written in C++ with a modular structure, which allows the user to program its own set of equations and use the finite volume tools embedded in OpenFoam to solve the specified problem. Although the basis of the transport equation was inspired by the previous work of  Horgue et al. (2015) , the structure of the muRT library is completely different, having been developed specifically for the conditions encountered for flow in aquifers and multicomponent reactive transport.