dataesr/scientific-paragraphs-categorization huggingface dataset:
lenticularis and O. heptagona, which confirms a low specificity. The compact mass within dinoflagellate cells could correspond to the trophont stage, an advanced stage of the infection caused by the parasitoid Amoebophrya  (Cachon, 1964; Chambouvet et al. , 2008). Infections of other dinoflagellate genera present in the same samples (e. g. Gambierdiscus spp. , Prorocentrum spp. ) have not been investigated and are possible. From a molecular point of view, the genotype of the parasitoid found in the present study is rather distant from Amoebophrya strains isolated from other dinoflagellates and only one sequence of this genotype was already available in GenBank (MK752531, Amoebophrya sp. isolate AT5), without any indication of the putative host and origin. This unexpected finding is an illustration of serendipity, and as it was not the focus of the study, several aspects regarding parasitism remain in question due to inappropriate sampling.
Acknowledgements The authors extend special to thanks to  Dr. Marc Gottshling  for his advices regarding phylogenetic analysis and for sharing his alignment of gonyaulacean dinophytes, and to  Dr Philipp Hess  for editing of the English language. Authors acknowledge the support of the  Collectivité Territoriale de Martinique  for allowing this project been carried out. AB wishes to express her deep gratitude to the  GDR Phycotox  and the members of the Club Soroptimist Diamant-Les Rivières. The  Regional Council of Brittany , the  General Council of Finistère , the urban community of  Concarneau Cornouaille Agglomération  and the  European Regional Development Fund (ERDF)  are acknowledged for the funding of the  Sigma 300 FE-SEM  of the  Concarneau Marine Biology Station.
It follows from Monte Carlo simulations of CALIOP signals that multiple scattering is of importance even though photon mean free paths are much larger than the footprint di-Published by Copernicus Publications on behalf of the European Geosciences Union. V. Shcherbakov et al. :  Empirical model of multiple-scattering effect on lidar data ameter, e. g. cirrus clouds or aerosol layers (see e. g. Winker, 2003). If the distance to a cloud or an aerosol layer is low, the footprint is rather small. For example, for a typical RFOV of 0. 25 mrad and distance to a layer of 8 km, the footprint diameter is 2 m; if the RFOV is 1. 0 mrad, the footprint diameter is 8 m. (Note that in this work RFOV refers to the full angle. ) If the distance to a layer is 1 km, e. g. an airborne lidar, the footprint diameters become 0.
Methodology and simulation conditions The principal tool to simulate lidar signals was the McRALI (Monte Carlo Radar Lidar) software developed at the Laboratoire de Météorologie Physique  (Alkasem et al. , 2017; Szczap et al. , 2021). The software employs a forward Monte Carlo (MC) approach along with the locate-estimates method to simulate propagation of radiation (see e. g. Marchuk et al. , 2013). McRALI is based on the 3DMCPOL model  (Cornet et al. , 2010). The polarization state of the radiation is computed using Stokes vectors and scattering matrixes of atmospheric compounds. It takes into account molecular scattering. In this work, the properties of the atmosphere were assigned according to the 1976 standard atmosphere  (NOAA, 1976). McRALI is a fully 3D software; that is, values of the extinc-tion coefficients, the single-scattering albedos, and the scattering matrixes are assigned in 3D space.
The values d eff = 56. 8 µm (the standard deviation 20. 1 µm) and d eff = 80. 0 µm (the standard deviation 24. 5 µm) correspond to the data for jet-stream (JS) cirrus clouds and cirrus clouds (Ci), respectively, of the work by  Gayet et al. (2006). The obtained scattering matrixes are in good agreement with the database from  Yang et al. (2013). The scattering matrix of the warm cloud was computed ac- cording to the Mie theory for water spheres having a gamma size distribution with d eff = 18. 0 µm (standard deviation of 5. 3 µm). The scattering matrix of the coarse aerosol was simulated according to the work by  Dubovik et al. (2006)  as the "Mixture 1" of spheroids with the distribution of axis ratios within the range [0. 3349, 2. 986] (assuming, as the firstorder approximation, that shape is independent of size).
the wrong choice of τ p (h b , h i ), that is, of the coefficient k, reverses the behaviour of the MSF η MS (h i ) at the cloud base. Data availability. The data of Monte Carlo simulations are available from the corresponding author upon request. Supplement. The supplement related to this article is available online at: https://doi. org/10. 5194/amt-15-1729-2022-supplement. Author contributions. VS developed the empirical model and Appendix A. FS and GM acquired funding. VS, FS, AA, GM, and CC contributed to developing the McRALI code, numerical simulations, and data treatment. VS wrote the manuscript with help from FS. Competing interests. The contact author has declared that neither they nor their co-authors have any competing interests. Disclaimer. Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Acknowledgements. This work is part of the  French scientific community EECLAT project (Expecting EarthCARE , Learning from A-train). The EECLAT community and research activities are supported by the  National Center for Space Studies (CNES)  and the  National Institute for Earth Sciences and Astronomy (INSU). Financial support. This research has been supported by the  National Institute for Earth Sciences and Astronomy  ( INSU grant ). Review statement. This paper was edited by  Alexander Kokhanovsky  and reviewed by three anonymous referees.
Materials and Methods Lake Geneva Time-Series Data and Reoligotrophication. Lake Geneva (Lac L eman) is a deep (310 m), large (589 km 2 ) lake located between Switzerland and France. Lake Geneva is arguably the cradle of European limnology due to the seminal interdisciplinary studies by Forel  (24). It is classified as a warm monomictic lake with intermittent deep mixing. The last complete winter mixing was observed in 2012. All of the time-series data, except the Rhone River discharge, are from long-term monitoring conducted by the Centre Alpin de Recherche sur les R eseaux Trophiques des Ecosyst emes Limniques (CARRTEL) laboratory and freely available in the Syst eme d'Observatoires, d'Exp erimentations et de Recherche en Environnement Observatoire des Lacs (https://si-ola. inra. fr). TP epi and CHL were volume-weighted averaged over the first 20 m of the water column.
The total amount of phosphorus TP lake was calculated by integrating over the hypsometry each bimonthly or monthly profile. Phosphorus concentration has been measured with two protocols, quantifying soluble-reactive phosphorus (SRP) and TP. However, these two measurements are tightly correlated (both integrated over the first 20 m or the full hypsometry) and give nearly identical results with EDM analysis. The SRP measurements give slightly higher predictive skill, however; thus, the results presented in the figures are those using SRP as an indicator of TP, rather than the measurement of TP. Rhone River data were obtained from the Swiss Federal Office for the Environment (https://www. hydrodaten. admin. ch/en/2009. html). Meteorological data were obtained from the Swiss Federal Office of Meteorology and Climatology, MeteoSwiss, for the monitoring station Pully (https://www.
meteoswiss. admin. ch/home/measurementvalues. html?param=messwerte-lufttemperatur-10min&station=PUY). The data derived from these sources needed for the calculations presented in the manuscript are included in SI Appendix and are also available at GitHub (https:// github. com/SugiharaLab/Geneva_Hybrid) and Zenodo  (43). Climate change and reoligotrophication scenario. This study did not use explicit regional climate predictions, such as those driven by greenhouse-gasemission scenarios for changes in atmospheric forcing, or future land-use scenarios for predicting changes in nutrient loading. Instead, we reanalyzed the 30-y historical data by applying synthetic TP lake and air-temperature time series. The air-temperature time series were generated by applying a uniform 0 °C, 1 °C, or 3 °C change to the detrended historical air temperature.
The TP lake time series were generated for a constant concentration from 65 to 15 μgÁL À1 (1-μgÁL À1 increments). The other meteorological drivers for Simstrat were unchanged, as we currently lack clear expectation for their fate under greenhouse-gas scenarios. The resulting Simstrat output for propagation of the air-temperature scenario into lake physics variables was then used to drive the EDM component. Our goal was to examine the benefits of a hybrid-modeling approach for addressing the complex challenges that will be confronting environmental management in the coming years. In principle, this same machinery can be generalized to follow any particular prediction of climate change or land-use. Equation-Based Model. We used a 1D hydrodynamic model [Simstrat version (v)1. 0 from https://github.
Schwefel et al. (21)  developed a two-box model of oxygen to run on output from Simstrat. The lake is divided into a surface layer and a deep layer, based on the time-varying thermocline depth (depth of maximum stratification indicated by the buoyancy frequency, N 2 ). Additionally, there is a seasonally varying input of oxygen from the Rhone River, with river flux in summer entering the surface layer and in winter entering the deep layer. Equation-Free Model. Comprehensive EDM analysis of DO and likely interactors was performed through a similar pipeline as analyses of planktonic food-web data in Deyle et al. (25)  and by using rEDM 0. 7. 3 from GitHub (https://github. com/SugiharaLab) (a newer application programming interface is now available through the CRAN repository, https://cran. r-project.
org/web/packages/rEDM/index. html). R-Markdown code to reproduce the EDM calculations is included in SI Appendix and is also available from GitHub (https://github. com/SugiharaLab/ Geneva_Hybrid). First, univariate analysis with simplex  (27)  and S-map  (29)  confirmed the presence of low-dimensional, nonlinear dynamics in DO B (SI Appendix). Thus, the basic assumption of EDM analysis is valid for these data. Next, CCM was computed between all variables using simplex projection  (26). Each pairwise test requires only a single fit parameter, the embedding dimension E. Following the insights of Ye et al. (45) , we selected embedding dimension E* in  [1, 15]  to maximize CCM skill at tp = 0 and then measure CCM skill at prediction-time tp = floor(ÀE*/2), the middle of the embedding vector. This mediates the risk of statistical overfitting without relying on univariate estimates of E that can be too low.
Measurement Data We test several snow samples composed of four different natural snow types, namely Precipitation Particles (PP), Depth Hoar (DH), Rounded Grains (RG), and large Rounded Grains (RGlr). The samples are prepared by sieving snow into small sample holders (diameter and height of 20 mm) and letting them sinter for a couple of days. Their microstructure is captured with X-ray tomography at a nominal resolution of 15 µm (Fig. 4 ). The cone penetration test is conducted with a modified version of the SMP, as shown in Fig. 5. More information on the sample preparation and the SMP measurement can be found in the study of  Peinke et al. (2020). The main sample properties are summarized in Tab. 1 and the measured profiles (one example for each snow type) are plotted in Fig. 6. The first 4 mm are affected by the progressive penetration of the conic tip and are not considered in the stochastic analysis  (Peinke et al.
For this interpretation, we have to remember that all R-values are detrended and normalized, thus the absolute values of the snow hardness R are not essential but more the resulting collective behavior of the snow types. As a last comment, we would like to point out that our characterization of a complex matter, here snow, by a penetration process should have the potential to be generalized to, for example, biological tissue or ground layers. Last but not least, we would like to point out that our work provides additional insight into analyzing and modeling the complex nature of snow types, but our intention is not to replace existing methods. Code and data availability. The code and data for this analysis can be found under https://github. com/pplin13/Snow_RCodes.
11. Collagenase solution: 5 mg/mL LiberaseTM resuspended in nuclease-free water. Store aliquots at À20 C. 12. Gelatin solution: 1 mg/mL gelatin. Heat to dissolve, filter. 13. Gelatin-coated P200 tips: pipette up and down gelatin solution, let the tips dry overnight, store at RT. 14. Gelatin-coated P1000 tips. 15. Trimmed P200 gelatin-coated tips: remove 1-2 mm of dry coated tip length using a scalpel blade. 24. DNA concentration fluorometer (e. g. , Qubit, Thermo Fisher Scientific). 25. MARS-Seq computational pipeline (see Note 6). 26. scRNA-seq data clustering pipeline (see Note 7). 3 Methods.
No statistics are presented for the Mexican ponds since one of the flux values was zero and could not be log-transformed. Existing empirical models  5, 34, 35, 37  weakly predicted CH4 dynamics in our study systems, and deviations from predictions were strongly linked to salinity (Fig. 5 ). The models we tested were developed based on regional and global data largely from freshwater systems  5, 34, 35, 37  , and use typical CH4 drivers including water temperature, river discharge, lentic surface area, chlorophyll a, TN and DOC concentrations. These models captured a third of the observed variability in pCH4 in our dataset (R 2 = 0. 32; Fig. 4a ), and only weakly predicted diffusive and ebullitive emissions from small lentic systems (R 2 = 0. 15 and 0. 03 respectively; Fig. 4b-c ). Salinity, not included in these models, explained 20, 24, and 42 % of the deviation from predicted pCH4, diffusion, and ebullition, respectively (Fig.
TN was measured after chemical digestion on an Alpkem analyzer (IO analytical Flow Solution 3100), a Lachat QuickChem 8500, or a Dionex DX600 instrument. For some sites, TP and TN analysis were performed on filtered samples (0. 45 μm) and thus represent the dissolved rather than the total P and N concentrations. However, where both the dissolved and total concentrations were measured, the dissolved fraction represented the vast majority of nutrient content (mean of 77 and 89% for P and N respectively). Therefore, we assumed the dissolved fraction to be representative of the total nutrient content in these samples. The small error that this introduces in the data was considered in the interpretation of results. Chlorophyll a concentration was measured in most lentic systems by spectrophotometry after filtration (on GF/F or GF/C filters) and alcohol extraction.
CH4 partial pressure and flux CH4 concentration in all samples was determined using the headspace technique, however, since the database is a post hoc combination of data collected independently by different research teams, details of the gas sampling method are not uniform. However, variation introduced by distinct methodologies is expected to be minor relative to the measured range of CH4 content across sites. Overall, water was collected in an airtight container (140 mL plastic syringe or 1. 2 L glass bottle or 160 mL Wheaton glass serum bottle). An air headspace was created inside the sealed container, either on site or in the laboratory (after preserving the water with potassium chloride). The headspace consisted of ambient air at the sampling site or ultrahigh purity dinitrogen in the laboratory, and the air to water ratio varied between 0.
Statistical analyses All statistical analyses were performed in R  69. Prior to analysis, data were log10 transformed if necessary to meet normality requirements. The links between pCH4 or flux and other variables were assessed via multiple linear regressions using the function 'lm', and verifying the homoscedasticity of the residuals with a Shapiro-Wilk test (function 'shapiro. test'). The same regression analysis was performed on standardized values (to a standard deviation of one) to compare effect sizes (coefficients) of explanatory variables (Fig. S2 ). A marginal effect analysis was performed for each system type using the R package 'sjPlot'  72  to visualize the effect of salinity on pCH4 when other variables are held constant (at dataset average). Measured pCH4, diffusive, and ebullitive fluxes were compared to predicted values based on existing literature models.
We selected multiple models that use established CH4 predictors measured in our dataset, and that are based on empirical surveys in Canada or at the global scale. pCH4 was modeled based on surface area, temperature, and TN for lentic systems (lakes, wetlands, and ponds)  35  , and on temperature, velocity, and DOC concentration for lotic systems  37. Both models are based on empirical linear relationships developed from large-scale surveys in the eastern Canada  35, 37. Predicted CH4 diffusive flux rate was calculated as the mean of two modeled values: a global model using area and Chl a concentration 5 , and a regional East-Canadian model using area and temperature  35. Likewise, predicted CH4 ebullition was considered as the mean of a global model based on a global relationship with Chl a 5 , and a regional (East-Canada) model using sediment temperature  34.
Temporal trends To explore temporal trends in SO4 2-concentration in the study region, we used a publicly available (Saskatchewan Water Security Agency), long-term monitoring dataset collected in southern Saskatchewan lentic systems between 1990 and 2020. A subset of sites was selected for long-term trend analysis based on the following criteria: 1) a minimum of 10 observations within the 1990 -2020 time period, 2) observations span at least 5 years, 3) at least one recent observation after 2010. Where all three criteria were met, a Sen slope analysis was performed using R package 'zyp' 77 to determine the trends in SO4 2-over the past 3 decades. For each Sen slope, a 95 % confidence interval (CI) was calculated and used to determine the significance of the slope, with trends considered significantly increasing or decreasing if the CI range was entirely positive or negative respectively (Fig.
Acknowledgments This study was funded by a  MITACS-Accelerate  grant with  Ducks Unlimited Canada , awarded to CS in collaboration with LEB and MJB, and a postdoctoral fellowship from the  National Scientific and Engineering Research Council (NSERC)  to CS. M. J. B was funded by awards from the  University of Lethbridge ,  NSERC ,  Canada Foundation for Innovation (CFI)  and the  Canada Research Chairs (CRC) program. PRL was funded by the  CRC  and  CFI programs, NSERC , the  Province of Saskatchewan , and the  University of Regina. KF was funded by the  Government of Saskatchewan  (grant no. 200160015 ) and  NSERC. BW was funded by  NSERC  ( Discovery Grant ) and the  Saskatchewan Ministry of Environment Fish and Wildlife Development fund. We thank  David Butman  and  Lisa Windham-Myers  for important discussions related to data analysis and interpretation.
(2010). Acknowledging this, the TEOS-10 incorporates the notion of "Density Salinity" or S A dens. S A dens is "the value of the salinity argument of the TEOS-10 expression for density which gives the sample's actual measured density at t = 25°C and p = 101 325 Pa", and it is proposed as an observational parameter that should provide an estimate of the S A. S A dens is also called Absolute Salinity or S A. However, the in-situ measurement of density is not a widespread practice owing to the lack of instrumentation available for the purpose on the market, and the best practical solution for measuring salinity continues to be the one employing conductivity. TEOS-10 recommends that the salinity reported in databases remains the S P of the PSS-78. It also recommends calculating S A as the sum: S A = S R + d S A (1) where S R is the Reference-Composition Salinity and dS A is the Absolute Salinity anomaly.
Relations between the speed of sound, salinity and density The ocean is a privileged environment for the use of acoustic techniques. The speed of sound c is a thermodynamic quantity directly linked to the adiabatic compressibility of the medium: c = c(S A , t, p) = ( ∂ P= ∂ r) 0:5 = (rk) -0:5 (3) where k is the adiabatic compressibility coefficient, also called isentropic and isohaline compressibility in the case of seawater. k is expressed in Pa -1. c is inversely proportional to density and directly related to S A , the temperature t expressed in °C and the pressure p. The TEOS-10 software libraries allow the calculation of c(S A , t, p). The table  O. 1 of appendix O of IOC et al. (2010)  specifies that the 1974 Del Grosso data has been used to determine the coefficients of the Gibbs potential in the  TEOS-10 (Feistel, 2003).
15 to 313. 15) K and in the absolute salinity range (10. 036 to 38. 201) g kg -1 , for pressures up to 70 MPa. They have found that, in the case of North Atlantic Seawater at atmospheric pressure, when experimental results are compared with those predicted by TEOS-10 equation of state, the agreement is generally better than 0. 03% (0. 45 m s -1 at 1500 m s -1 ). At higher pressures, speed of sound results, measured in IAPSO Standard Seawater, show an agreement on the order of 0. 06% (0,90 m s -1 ) with the exception of the sample with absolute salinity equal to 10. 036 g • kg -1. Allen et al. (2017) proposed a new salinity equation based on sound speed using a dataset consisting of values of temperature (0 -40°C), salinity (0 -40, PSS-78) and pressure (0 -6000 dbar). Calculating the sound speed with the  Chen and Millero (1977)  relation, the authors defined Practical Salinity as: S p = S 0 + DS t + DS p + DS c + DS tpc (4) where DS t , DS p , DS c and DS tpc are the components of an 81term sixth order polynomial determined by applying the Gauss least squares method.
They estimated their measurement uncertainty for R. I. to be 3 x 10 -5. These data were used later by Millard and Seaver to build their algorithm. In  Mahrt and Waldmann, 1988  developed a "Fiber Optical Point Refractometer based on the principle of grazing incidence". The light from the source was guided by an optical fiber to strike a prism at the grazing incidence. According to the R. I. of the water, the beam was refracted with a variable angle onto a position detector. The instrument was 40 cm long with a diameter of 5 cm, adapted to high pressures, able to sample very quickly (1000 samples/s), and presented a precision of 10 -6 for the R. I. determination. The problem with the grazing incidence technique is that the measurement is made in the thermal limit layer of the prism. During profiling, this layer is where thermal exchanges occur between the prism and the medium.
Data analysis For each instrument, the data quality assurance (QA) procedures, where applicable, are outlined in the Method Section for each instrument. For the AiRRmonia, MARGA, QCLAS, and the miniDOAS instruments, the zeros and/or calibration standards used are described in Sect. 2. 2. 1, 2. 2. 3, and 2. 2. 7, respectively. They were applied to the datasets prior to undertaking the data analysis presented in Sect. 3. No other LTMHTR instrument had any zero or calibration applied, as the instrument manufacturers described the methods as "calibration free" at the time of the study, so they were only operated with the manufacturer factory calibrations. Data which did not meet the QA was not included in the analysis; further details are found in the Sect. 3. 2. Measurements provided in units of µg m -3 were converted to parts per billion (ppb) using the temperature and pressure measured at the Easter Bush site.