dataesr/scientific-paragraphs-categorization huggingface dataset:
The second step is the treatment of the scattering problem. The calculation is carried out in the non-relativistic distorted wave approximation using the UCL distorted wave (DW) program  [16]. The reactance matrices are calculated in LS coupling. The program JAJOM  [17]  uses these reactance matrices and the TCC to calculate collision strengths in intermediate coupling. In the present work, we have transformed JAJOM into JAJPOLARI (Elabidi and Dubau, unpublished results) to produce the collision strengths and the reactance matrices R in intermediate coupling, which will be used by the program RtoS (Dubau, unpublished results) to evaluate the real and the imaginary parts of the scattering matrix according to Re S = 1 -R 2 1 + R 2 -1 (3) and Im S = 2R 1 + R 2 -1. ( 4 ) The two expressions (3) and (4) have been deduced from the relation S = (1 + iR)(1 -iR) -1 , and such expressions guarantee the unitarity of the S-matrix.
Finally, in the code JAJPOLARI, the reactance matrices R I in intermediate coupling corresponding to the initial I level are evaluated for each channel and at a total energy E I = E i + ε. The same procedure is done for R F but at a total energy E F = E f + ε. E i (E f ) are the energies of the initial (final) atomic levels. The program RtoS receives R-matrices and transforms them into real and imaginary parts of S-matrices according to Equations (3) and (4) at total energies E I and E F , and combines a given matrix element S I for an initial level I with a number of matrix element S F for the final level F. The obtained matrix elements Re S and Im S enter into Equation  (2). The integral over the Maxwell distribution (Equation (  1 )) is evaluated numerically using a trapezoid integration with a variable step to provide the line width W.
Atomic Structure and Electron Scattering Data We have used the following nine configurations in our calculation: 1s 2 2s 2 2p 6 : 3s 2 , 3s3p, 3p 2 , 3s3d, 3p3d, 3s4s, 3s4p, 3s4d, and 3s5s, which give rise to 38 levels, which are listed in Table  1  with their energies in cm -1. These values have been compared with the observed ones taken from the tables of the National Institute of Standards and Technology database: NIST  [22]  which are originally from Saloman  [23]. We compare also with the energies computed using the multiconfiguration Hartree-Fock method (MCHF)  [24]  and with those obtained using the AUTOSTRUCTURE code  [25]. The averaged disagreement between these three results is less than 1%. We detect an inversion between the two levels 10/13 and 25/26 regarding those of NIST and MCHF.
This inversion does not affect the calculations since the agreement is still acceptable (about 5%). Table  1. Our present fine-structure energy levels E (in cm -1 ) for Ar VII compared with those of NIST  [22] , with those obtained from the multiconfiguration Hartree-Fock method (MCHF)  [24] , and with those from the R-matrix calculation (AS2014)  [25]  We present also in Table  2  radiative decay rates A ij , weighted oscillator strengths g f , and line strengths S for some Ar VII lines up to the level 14 (3s3d 1 D 2 ). Our A ij values have been compared with those obtained from the AUTOSTRUCTURE code  [25] , and with those from the SUPERSTRUCTURE code  [26]  using five configurations (1s 2 2s 2 2p 6 : 3s 2 , 3s3p, 3p 2 , 3s3d, and 3s4s). The averaged difference is about 20% with the results of  [25]  and about 24% with those of Christensen et al.
[26]. Some transitions present a high difference, especially those for which A i-j are relatively small (about 10 6 s -1 and below). The g f values have been compared only with Christensen et al. [26]  and the difference is about 24 %. The g f values are calculated in  [26] , but we took them from the database CHIANTI version 8. 0  [27]. With the code JAJOM, fine structure collision strengths are calculated for low partial waves l of the incoming electron up to 29. For large partial waves l, this method becomes cumbersome and inaccurate, but their contributions to collision strengths cannot be neglected. For 30 ≤ l ≤ 50, two different procedures have been used: for dipole transitions, the contribution has been calculated using the JAJOM-CBe code (Dubau, unpublished results) based upon the Coulomb-Bethe formulation of Burgess and Sheorey  [28]  and adapted to JAJOM approximation.
Line Broadening Results Two methods for line broadening calculations have been used in our work. The first is the quantum mechanical approach (Q), and the second is the semiclassical perturbation method SCP. To evaluate the line broadening through the second method, we need atomic parameters such as energy levels and oscillator strengths. In our SCP calculations (SCP SST ), we have taken atomic data of the code SST  [14]. We compare our results (Q and SCP SST ) to the SCP calculations (SCP BD ) performed in  [12] , where atomic data have been taken from the method of Bates and Damgaard  [13]. This method has been used many times with different ions, and it has been shown that the corresponding results (using the Bates and Damgaard or the SST data) are in good agreement with experimental and other theoretical results  [31] [32] [33].
Many of these SCP results have been stored in the database STARK-B  [34]. We have performed quantum (Q) and semiclassical perturbation (SCP) Stark broadening for 12 lines of Ar VII for electron temperature range (2 -50) × 10 4 K and at electron density N e = 10 18 cm -3. We present our results in Table  4  for transitions between singlets, in Table  5  for the resonance line 3s 2 1 S 0 -3s3p 1 P o 1 , and in Table  6  for transitions between triplets. A comparison was made between our quantum and our semiclassical perturbation results SCP SST in Tables  4  and 5. We also included the semiclassical results SCP BD  [12]  in Table  6  in our comparison. Tables  4  and 6  show that the quantum line widths are always higher than the two semiclassical ones (SCP SST and SCP BD ). We also found that, except for the resonance line, the ratio Q SCP increases and decreases with temperature.
Conclusions We have calculated in the present work quantum and semiclassical perturbation Stark broadening parameters for 12 Ar VII lines at electron temperatures from 2 × 10 4 to 5 × 10 5 K and at electron density N e = 10 18 cm -3. The structure and collision problem has also been treated for this ion. We have used nine configurations (1s 2 2s 2 2p 6 : 3s 2 , 3s3p, 3p 2 , 3s3d, 3p3d, 3s4s, 3s4p, 3s4d, and 3s5s). The structure and collisional parameters have been used in our quantum mechanical line broadening calculations. Since it is important to check their accuracy, we compared our energies to those of  [22] , to those obtained by the multiconfiguration Hartree-Fock method  [24] , and to those obtained from the AUTOSTRUCTURE code  [25]. An acceptable agreement was found with the NIST results (better than 1%).
We also compared our A ij values with those obtained from the AUTOSTRUCTURE code  [25]  and with those from the SUPERSTRUCTURE code  [26]  using five configurations (1s 2 2s 2 2p 6 : 3s 2 , 3s3p, 3p 2 , 3s3d, and 3s4s). The averaged difference is about 20% with the results of  [25]  and about 24% with those of Christensen et al. [26]. The oscillator strengths have been compared only with the results of Christensen et al. [26] , and we found an averaged agreement of about 24%. The electron-ion collision process was also studied, and collision strengths from the lowest five levels to the first 14 levels are presented at three electron energies 7. 779 Ry, 13. 674 Ry, and 23. 336 Ry. The comparison with the collision strengths of Christensen et al. [26]  indicates an agreement (averaged over the considered transitions and energies) of about 20%.
Acknowledgments: This work has been supported by the  Tunisian Research Unit UR11ES03  and the  French one UMR   8112. It has also been supported by the  Paris Observatory  and the  CNRS. We also acknowledge financial support from the  "Programme National de Physique Stellaire" (PNPS)  of  CNRS/INSU ,  CEA , and  CNES, France. This work has also been supported by the  Ministry of Education, Science and Technological Development of Serbia  (project  176002 ). Some results have been taken from the database CHIANTI, which is a collaborative project involving  George Mason University , the  University of Michigan (USA) , and the  University of Cambridge (UK).
Introduction GammaGompertzCR is an R package (R Core Team 2016) that allows estimating survival in free-ranging animal populations using a Gompertz capture-recapture model with a Gamma frailty to deal with individual heterogeneity  (Marzolin, Charmantier, and Gimenez 2011; Missov 2013). To use the package, users should be familiar with Bayesian MCMC techniques and in particular how to interpret convergence diagnostics. We refer to  Robert and Casella (2010)  for an introduction to Bayesian MCMC techniques with R and to  King et al. (2009)  for an introduction for ecologists. In this paper, we introduce the theory underlying the model we implement in GammaGompertzCR, and illustrate the approach using a real example.
Parameter estimation To get maximum likelihood parameter estimates, we use data cloning in the Bayesian framework  (Lele, Dennis, and Lutscher 2007)  implemented in the R package dclone (Solymos 2010). Data cloning uses multiple copies of the data to produce prior-invariant inferences and converge towards a normal distribution centered at the maximum likelihood estimates. In addition, this method allows detecting non-identifiable parameters  (Lele, Nadeem, and Schmuland 2010).
Illustration We now illustrate the estimation of Gamma-Gompertz model parameters using data cloning. We analysed capture-recapture data collected on 75 breeding females over 9 years in a Dipper (Cinclus cinclus) population  (Marzolin, Charmantier, and Gimenez 2011). These data are just a subset of the complete dataset (>1000 individuals, 35 years) that is provided with the package. We used 1, 10, 50 and 100 clones with 3 parallel MCMC chains for a total of 5000 iterates with a burn-in period of 1000. The model fitting took less than 5 minutes. 1 : Parameter estimates of the Gamma-Gompertz model using the Dipper dataset. We refer to Theory section for more details about these parameters. We provide the posterior mean and standard deviation as well as the 95% credible interval.
With respect to qualitative aspects, the incompleteness of the reference databases, the choice of the DNA marker and the efficiency of the PCR primers have been identified as important biases affecting species detection using DNA metabarcoding  (Pawlowski, Lejzerowicz, Apotheloz-Perret-Gentil, Visco, & Esling, 2016). For benthic diatoms, the rbcL gene has proved to be an appropriate taxonomic marker for biomonitoring  (Kermarrec et al. , 2013 (Kermarrec et al. , , 2014;; Vasselon, Domaizon, et al. , 2017; Vasselon, Rimet, et al. , 2017)  and a well-curated barcode reference library is already available in open-access to assign species names to rbcL sequences (R-Syst::diatom,  Rimet et al. , 2016). However, no clear relationship has yet been demonstrated between the relative species abundances obtained by DNA metabarcoding with the rbcL barcode and those obtained by morphological observations  (Rimet et al.
In order to confirm this hypothesis, we firstly conducted experiments on eight pure diatom cultures to examine whether variation in rbcL gene copy number per cell correlates with morphological characteristics (e. g. biovolume, cell length), from which a CF might be calculated. Secondly, the efficiency of the proposed CF was tested on (1) mock communities made by mixing known proportions of the eight diatom species cultures and (2) environmental diatom assemblages from rivers previously sequenced  (Vasselon, Rimet, et al. , 2017)  and for which data are available online  (Vasselon, Rimet, et al. , 2017  dataset, https://doi. org/10. 5281/zenodo. 400160). Last, the capacity of the CF to improve the ecological assessment of rivers was tested by comparing water quality index values calculated from molecular data with corrected abundances to those calculated from classical morphological abundances.
Diatom cell concentrations and proportions of live/dead cells were obtained by counting at least 400 specimens using inverted microscopy (×1,000 magnification) and the standard Utermöhl technique (European Committee for Standardization (CEN) 2006) (Figure  1 ). The proportion of live/dead cells was estimated by considering cells without visible intracellular contents as dead. Only living cells were taken into account to calculate the diatom cell concentration per ml of media. Flow cytometry using Sytox-Green was also used to confirm the microscopical data (not shown). RbcL copy number per ml was estimated by qPCR. From each cultivation replicate, 10 ml of culture was centrifuged at 17,000 × g for 30 min (Figure  1 ). Total DNA was extracted from the resulting pellet using a protocol based on GenEluteTM-LPA DNA precipitation (Sigma-Aldrich, St Louis, Missouri) as previously described  (Vasselon, Domaizon, et al.
(2017) , using a final volume of 25 μl using mix preparation and reaction conditions as described in Table  S1. A fluorescence threshold of 0. 01 was used to allow comparison of qPCR assays, denoising and determination of the cycles' threshold (Ct). Data analysis was performed using the rotor-gene Q Series software (version 2. 3. 1) and the rbcL copy per ml of media was determined. Finally, the number of rbcL gene copies per diatom cell was calculated for the eight diatom species by dividing the rbcL concentration (qPCR data) by the living cell concentration (microscopy data). A Kruskal-Wallis test was performed using r (R Development Core Team, 2013) to determine if the rbcL gene copy number per diatom cell varied significantly between the eight diatom species. Then, we tested the level of correlation between the number of rbcL gene copies per diatom cell and several morphological characteristics of the diatom cells (Table  1 ).
| Mock communities The calculated CF was applied to metabarcoding data obtained from controlled diatom mock communities. Five mock communities (M1 to M5) were created by mixing DNA extracted from each of the eight diatom species sampled during their exponential growth phase, and for which the correspondence between cell abundances (microscopy) and qPCR counts was known. For each of the five mock communities, the volume of DNA used for seven species was kept unchanged (1 μl) and only the volume of DNA of P. viridiformis varied as followed: M1 = 0. 2 μl, M2 = 0. 4 μl, M3 = 0. 8 μl, M4 = 1. 6 μl, M5 = 3. 2 μl. This resulted in contrasted rbcL proportions of the eight species among the five mock communities. Then, HTS sequencing of the rbcL 312 bp fragment was performed on three replicates of the five mock communities.
The 15 corresponding libraries were prepared following the method described by  Vasselon, Domaizon, et al. (2017)  with the same primers and PCR reaction conditions as those used for rbcL qPCR (Table  S1 ), changing only the cycle number to 30. Each library was diluted to 100 pm and all 15 were pooled together for one HTS run performed on the PGM Ion Torrent machine by the "Plateforme Génome Transcriptome" (PGTB, Bordeaux, France). The sequencing platform provided a unique fastq file for each of the 15 libraries containing demultiplexed DNA reads without the sequencing adapters. Quality filtering of DNA reads was performed using the mothur software  (Schloss et al. , 2009)  and bioinformatics process described previously  (Vasselon, Domaizon, et al. , 2017; Vasselon, Rimet, et al. , 2017).
| Environmental diatom assemblages To evaluate the efficiency of the CF to improve metabarcoding quantification from environmental samples, we used rbcL HTS data obtained from  (Vasselon, Rimet, et al. , 2017) , corresponding to 80 benthic diatom samples collected from rivers in tropical island of Mayotte, Indian Ocean  (Vasselon, Rimet, et al. , 2017 dataset,  https://doi. org/10. 5281/zenodo. 400160). A CF was calculated for each species (or genus when the species level was not reached) detected in molecular inventories of the rivers of Mayotte island using a generalized average of the morphological information (e. g. biovolume, length) available in the R-Syst::diatom library and applied to HTS data. Corrected molecular inventories were produced for all the 80 river samples using the CF.
The impact of the CF on diatom taxa abundance rank in the molecular inventories was assessed by comparing original and corrected molecular diatom inventories. Then, the Specific Pollution-sensitivity Index (SPI) used for ecological assessment was calculated for each sample based on the corrected diatom molecular inventories using the omnidia 5 software  (Lecointe, Coste, & Prygiel, 1993; library 5. 3 2015)  and compared to the morphological SPI values for all river samples  (Vasselon, Rimet, et al. , 2017). Pearson correlation was used to evaluate the strength of correlations between original or corrected molecular SPI values and the morphological SPI values. Wilcoxon Signed Rank tests were conducted to determine whether the difference between the molecular and the morphological SPI (ΔSPI) varied significantly when using the original or the corrected molecular data for the molecular SPI calculation.
The rank of the eight species was also affected; for example in M5 the application of the CF changed the proportion of P. viridiformis from 57% to 4% and the proportion of A. minutissimum from 4% to 42%. The correspondence between morphological and molecular relative abundances was highly improved by applying the CF on the HTS data (Figure  4a, b ). F I G U R E 2 Estimation of the rbcL copy number per diatom cell for the eight diatom species. Mean values calculated using the gene and the diatom cell concentrations obtained, respectively, by qPCR and inverted microscopy at T0, T1 and T2 sampling points (n = 9) F I G U R E 3 Correlation between the diatom cell biovolume and the rbcL gene copy number per cell after log(x + 1) transformation T A B L E 2 CF calculated for the eight diatom species using their respective cell biovolume (Table  1 ) and the linear equation between the rbcL copy number and the cell biovolume (Figure  3 ) From the 80 environmental samples previously sequenced  (Vasselon, Rimet, et al.
Limitation by mineral nutrients may also have an impact; for instance iron limitation can reduce the number of the chloroplast per cell (from 4 to 2) and their size in the marine diatom Thalassiosira oceanica (Hustedt) Hasle et Heimdal  (Lommer et al. , 2012). Variation in the cell physiological state was not taken into account in developing CFs for diatom metabarcoding. However, during our experiments we discriminated live and dead cells; we observed that their respective proportions did not affect significantly the correlation between the gene copy number per cell and the cell biovolume (Figure  S1 ). Further experiments should be performed to evaluate the impact on the final CFs of rbcL gene copy number variation linked to physiological status. The biovolume of each diatom species is required to apply the CF and hence correct the quantification in metabarcoding datasets.
Several reference databases provide biovolume information for a lot diatom species (e. g. Rimet et al. , 2016) , but they do not generally account for biovolume variability, which is a complicating factor in diatoms because of the peculiarities of the life cycle. Diatom cell size within a population is not constant due to the method of vegetative reproduction, which leads to a progressive cell size reduction in the population  (Crawford, 1981) , followed by restoration of cell size via a sexual event. For this reason, different cell sizes can be observed in the same diatom population, either in pure cultures (e. g. in the marine diatom Thalassiosira weissflogii Grunow:  Armbrust & Chisholm, 1992)  or in environmental populations (e. g. the freshwater species Sellaphora pupula (Kützing) Mereschk:  (Mann, Chepurnov, & Droop, 1999) ).
In the same way, (Vivien et al. , 2016) have shown that application of a CF to correct DNA reads proportions allows a more accurate estimation of oligochaete proportions, improving quality index calculation and quality assessment of watercourse sediments. Our results confirm that water quality index based on diatom metabarcoding and DNA read proportions are directly affected by gene copy number variation, and show the potential value of integrating CFs into molecular SPI calculation. However, as the biovolume-copy number relationship was based on only eight diatom species and the efficiency of the resulting CFs validated on only one HTS dataset, further experiments including more species and larger datasets will be required to develop and fully validate CFs for use in molecular biomonitoring.
1125 cm), measuring in addition slower dissociating complexes, were mounted on the SeaExplorer. Lehto et al. 27  used an even thinner diffusive domain (0. 0081 cm) than we did for our fast DGT, but the thin Nuclepore membrane involved (0. 001 cm) was only used in laboratory conditions and never in the field. Labile trace metal data and cellular metal quotas in plankton averaged over a time scale of a month will be used to estimate potential plankton growth limitation in the Mediterranean Sea. Such information was not previously available. In addition, average lability and dissociation rate of each sub-pool of the total dissolved trace metal concentration (free metal, inorganic and organic complexed metal) will be calculated using a new model.
Using cellular quotas that are 20% lower for Fe and 39% for Co, does not change the potential limiting effect of both elements for diatoms in the Mediterranean Sea. For all other plankton taxa and for Cd, Mn and Ni in diatoms, the cellular quotas are lower than their ratios in seawater. Thus, for flagellates and picoplankton, phosphate remains the main growth limiting element. Is the lability of the trace metals measured by the DGTs, however, as high as that of phosphate, which is present as free ion and, thus, directly available to plankton? Therefore, a kinetic analysis of the metal complex pools in the fast and in the classic DGT devices, including their dissociation rates and half-lives, was made. Average lability and dissociation rate of inorganic and organic metal complexes. Using literature data (such as total dissolved concentration of each metal, c T,M , and its percentages free, inorganic and organic), we can apportion, as a first approximation, the total dissolved amount into three pools: the free metal pool ⁎ c M , an inorganic pool ⁎ c ML in and an organic pool ⁎ c ML org.
Element c T,M Free metal DGT-fast DGT-classic ). The solubility of iron in seawater is very low, therefore much of the dissolved iron is present as small colloids (>0. 02 μm) and, thus, likely subject to aggregation and scavenging removal. No data could be obtained for Fe with the classic DGT, so no "general" treatment can be applied. However, according to Stockdale et al. 47  , soluble Fe-complexes are 98. 8% organic, so we can neglect other contributions and write: ε 1 ξ c 1 ξ 1 f k d,1 ε 2 ξ c 2 ξ 2 f k d,2 ( ξ ε ≈ ⁎ c c ( 11 ) DGT,Fe fast org,Fe org,Fe FeL org Taking c T,Fe = 5. 54 nM  41  and ε org,Fe = 0. 57 (as in the case of Cu), one gets ξ org,Fe f = 0. 152. More robust is that we find that the product ε ξ org,Fe org,Fe f is 0. 0087, reflecting the low mobility and lability of this pool.
Acknowledgements We acknowledge the skillful assistance of ALSEAMAR personnel for the preparation and the operation of SeaExplorer SEA010. We thank  HERCULES Foundation  (code:  UABR/11/010 ) for funding of the High Resolution-ICPMS and SeaExplorer procurement sponsors DEME Group and  Dr. G. Decadt. Support of this research from the  Spanish Ministry MINECO  (Project  CTM2016-78798 ) is acknowledged.