dataesr/scientific-paragraphs-categorization huggingface dataset:
Experimental results The quality of our system is evaluated using two classical metrics: the character error rate (CER), i. e. the edit distance between the reference and the prediction computed at the character level, 10 and the word error rate (WER), a similar metric computed at the word level. Note that what makes the use of the latter metric possible is that the systems we trained are capable of predicting word boundaries (which was not the case in previous work such as  Adams et al. 2018). Using a ten-hour corpus for fine-tuning XLS-R, the system obtains a CER of 7. 4 % and a WER of 18. 5 %. Figure  1  shows how the performances of a guage documentation setting: it constitutes common practice in character-level ASR. 9 https://huggingface. co/datasets/ BenjaminGalliot/pangloss 10 Our system is predicting a stream of characters and not of phonemes (as stated in §2, the label set is made of the characters used to write the phonemes) and the edit operations, at the heart of the CER computation, are defined directly on the characters.
Databases or bioinformatics programs, such as Aramemnon  [67] , TMPred  [68] , TMHMM  [69] , PredGPI  [70]  or GPI-SOM  [71] , can be used to this end. The ProtAnnDB annotation tool collects such predictions for 21 plant species  [72]. Other proteins expected to be intracellular have also been identified in cell wall proteomes (Section 5). They could be considered as contaminant proteins or as non-canonical CWPs. However, one cannot exclude the existence of alternative routes of secretion which have been demonstrated in bacteria and in mammals for which dedicated software has been designed (Secretome P)  [14]. In all the cell wall proteomes included in this study, we have chosen to consider proteins as CWPs if (i) a signal peptide could be predicted by at least two different bioinformatic programs, (ii) no ER retention signal could be predicted and (iii) less than two trans-membrane domains could be predicted, or if an experimental work already showed that proteins of the same family were located in the extracellular space.
• Proteins acting on cell wall carbohydrates (PACs) belong to the major functional class in all the cell wall proteomes accounting for up to 25% of the CWPs. It comprises expansins  [79]  as well as glycosyl hydrolases (GHs), carbohydrate esterases (CEs) such as pectin methylesterases (PMEs) and polysaccharide lyases (PLs). The description of the latter protein families can be found in the Carbohydrate-Active enZYmes Database (CAZyDB, http://www. cazy. org) (accessed on 6 April 2022)  [80].
In particular, mutants impaired in AtFLA11 and AtFLA12 exhibit reduced tensile strength and stiffness  [134]. In this case, interactions between FLAs and cellulose microfibrils were suspected. Furthermore, in the functional class comprising signaling molecules, proteins with leucine-rich repeats (LRRs) are found in all cell wall proteomes. Their role is not clear but they could interact with other proteins or with peptides. Such interactions have been reported for the LRR domains of AtLRX2 and AtLRX8 interacting with the rapid alkalinization factor 4 (RALF4) signaling peptide  [135]. The DUF 642 (domain of unknown function 642, InterPro domain IPR006946) proteins were initially identified as major proteins in the cell wall proteome of A. thaliana etiolated hypocotyls  [31]. The DUF 642 domain is frequently associated with a galactose-binding-like domain (InterPro domain IPR008979).
What about the Non-Canonical Proteins Identified in Cell Wall Proteomes? All the published proteomes characterized from purified cell walls, extracellular fluids or cell suspension culture media contain proteins which are not expected to be secreted. These proteins have now been included in a new version of the plant cell wall proteome database called WallProtDB-2 (https://www. polebio. lrsv. ups-tlse. fr/WallProtDB/) (accessed on 6 April 2022) to allow obtaining an overview of their predicted sub-cellular localization and biological activity. Apart from the 4292 proteins considered to be bona fide CWPs (Section 3), WallProtDB-2 now contains 6462 proteins presumed to be intracellular and identified in apoplastic fluids or among proteins extracted from purified cell walls (Table  2 ). These proteins are assumed to be non-canonical CWPs.
Similar results were observed for IPR domains with 938 domains (about one third) only present in one protein and 36 domains present in more than 50 proteins (Appendix A). The number of proteins sharing a given domain increases with the number of presumed contaminants in a given cell wall proteome. Figure  5B  illustrates the case of proteins predicted to have a IPR ribosomal domain. Among these domains, there are (i) structural domains such as PF00076 (RNA recognition motif) shared by 174 proteins and IPR016040 (NAD(P)-binding domain) shared by 315 proteins or (ii) domains corresponding to a biological activity such as PF00012 (Hsp70 family) shared by 67 proteins, and IPR013766 (thioredoxin domain) shared by 159 proteins (Appendix A). The top 20 most represented Pfam domains describing a biological activity are listed in Table  3.
None of these functions have already been described in the extracellular space. The frequent identification of certain proteins in cell wall proteomes may have different explanations: (i) they could exhibit specific features allowing them to strongly interact with cell wall components during the purification of cell walls, for example, the histones (61 entries in 7 plant species, PF00125, IPR007125), which are basic proteins like most CWPs  [12] ; (ii) they could be very abundant proteins such as ribosomal proteins (altogether 578 entries in 8 plant species); or (iii) secreted through alternative secretory pathways. For some protein families, there is no clear hypothesis regarding their presence in many cell wall proteomes: e. g. , thioredoxin (e. g. , PF00085 with 117 occurrences in 12 plant species), heat-shock proteins (e.
g. , PF00012 with 67 proteins in 12 plant species), glyceraldehyde 3-phosphate dehydrogenase (PF02800 and PF00044 with 46 and 45 proteins in 11 and 10 plant species, respectively), lactate/malate dehydrogenase (PF02866 and PF00056 with 49 proteins in 10 plant species) and cyclophilin type peptidyl-prolyl cis-trans isomerase (42 proteins in 9 plant species). Finally, these proteins could be moonlighting ones, being present in different cell compartments and having different functions in each of them  [138]. As an example, two non-specific lipid transfer proteins of A. thaliana, AtLTP2 and AtLTP4, have been localized in both the cell wall and chloroplasts  [120, 132]. As mentioned above, UPS pathways have been described in bacteria and mammals. In plants, the best documented example of the presence of leaderless proteins in the apoplast is probably that of the leaderless jacalin-related lectin of Helianthus annuus (Helja): it has been identified in extracellular fluids  [139] , and in extracellular vesicles  [140] , and it has been immunolocalized in the extracellular matrix  [139].
Supplementary Materials: The following supporting information can be downloaded at: https: //www. mdpi. com/article/10. 3390/ijms23084273/s1. Author Contributions: Conceptualization, E. J. ; methodology, E. J. and H. S. C. ; database, H. S. C. ; validation, E. J. and H. S. C. ; formal analysis, H. S. C. and H. K. ; investigation, H. K. , H. C. and E. J. ; data curation, E. J. and H. S. C. ; writing-original draft preparation, E. J. ; writing-review and editing, H. K. , H. C. and E. J. ; visualization, E. J. ; supervision, E. J. ; project administration, E. J. ; funding acquisition, E. J. All authors have read and agreed to the published version of the manuscript.
Large rivers play an important role in transferring water and all of its constituents including carbon in its various forms from the land to the ocean, but the seasonal and inter-annual variations in these riverine flows remain unclear. Satellite Earth observation datasets and reanalysis products can now be used to observe synoptic-scale spatial and temporal variations in the carbonate system within large river outflows. Here we present the OceanSODA-UNEXE time series, a dataset of the full carbonate system in the surface water outflows of the Amazon (2010-2020) and Congo Rivers (2002-2016). Optimal empirical approaches were used to generate gridded Total alkalinity (TA) and dissolved inorganic carbon (DIC) fields in the outflow regions. These combinations were determined by equitably evaluating all combinations of algorithms and inputs against a matchup database of in situ observations.
Gridded TA and DIC along with gridded temperature and salinity data enable the calculation of the full carbonate system in the surface ocean. The algorithm evaluation constitutes a Type A uncertainty evaluation for TA and DIC where model, input and sampling uncertainties are considered. Total combined uncertainties for TA and DIC were propagated through the carbonate system calculation allowing all variables to be provided with an associated uncertainty estimate. In the Amazon outflow, the total combined uncertainty for TA was identified as 36 μmol kg -1 (weighted RMSD 35 μmol kg -1 and weighted bias 8 μmol kg -1 for n=82) and for DIC was 44 μmol kg -1 (weighted RMSD 44 μmol kg -1 and weighted bias -6 μmol kg -1 for n=70). The spatially averaged propagated uncertainties for the partial pressure of carbon dioxide (pCO 2 ) and pH are 85 μatm and 0.
, 2020). Satellite Earth observations provide a means of accurately assessing the carbonate content of large rivers by utilising oceanographic variables that can now be routinely observed from space combined with published empirical algorithms from the literature  (Land et al. , 2019). We present the University Of Exeter (UNEXE) OceanSODA dataset (OceanSODA-UNEXE), decadal datasets of riverine carbonate system variables for the two largest rivers in the world by discharge, the Amazon and the Congo Rivers  (Dai and Trenberth, 2002). This paper details how the optimal combination of empirical algorithms and Earth observation datasets were selected and used to construct OceanSODA-UNEXE. This paper provides an assessment of the uncertainty associated with the key TA and DIC parameters using a standardised uncertainty framework and a large in situ database.
Statistical terms overview Root mean squared difference (RMSD) is a measure of accuracy and is calculated as the square root of the average of squared errors e. g. RMSD = ((Σ(x 0x 1 ) 2 )/n) 1/2 , where x 0 are the estimated values, x 1 are the reference values and n is the number of observations. The bias of a dataset is defined as the mean difference between the estimation and reference, Bias= Σ(x 0x 1 ))/n. The mean absolute difference (MAD) of a dataset is a measure of variability and is calculated as MAD = (Σx -𝑥̅ )/n, where 𝑥̅ is the mean. The correlation coefficient (r ij ) is a measure of linear correlation between the estimate and the reference variables and is defined as r ij = (Σ (x i -𝑥̅ i ) (x j -𝑥̅ j )) / (Σ (x i -𝑥̅ i ) 2 Σ (x j -𝑥̅ j ) 2 ) 1/2. Uncertainty representation and the terminology used throughout this paper are consistent with the International Bureau of Weights and Measures (BIPM) Guide to the expression of uncertainty in measurement (GUM) methodology  (JCGM, 2008).
Weighted statistics allow uncertainties in the reference dataset (in situ measurement uncertainty in this case) to be accounted for within the performance analysis (i. e. the reference data are not considered 'truth' as they also contain uncertainties). Weights are calculated as the sum of the individual weight of each algorithm (w), where w = 1/((in situ measurement uncertainty) 2 +(literature algorithm uncertainty) 2 ) 1/2  (Ford et al. , 2021). For clarity and easy comparison to previous published https://doi. org/10. 5194/essd-2022-294 Preprint. Discussion started: 2 September 2022 c Author(s) 2022. CC BY 4. 0 License. work both weighted and unweighted statistics of all metrics are given, for example weighted RMSD (wRMSD) is calculated as wRMSD= (Σ(weights*(x 0x 1 ) 2 )) ½. When evaluating algorithms by wRMSD, a further issue can arise where the valid region over which each algorithm can be applied overlaps with different in situ data.
For example, an algorithm evaluated using data from highly-variable coastal waters may have a higher wRMSD than another algorithm evaluated using solely data from a less variable open ocean region; in this scenario it may be falsely concluded that the coastal ocean algorithm performs worse. This is a clear weakness of comparing wRMSD values from different sources and across differing regions  (Land et al. , 2019). Following the methodology of  Land, Findlay et al. (2019)  we derive RMSDe from wRMSD, RMSDe is a more representative metric to compare accuracies as it allows algorithms to be evaluated in a like for like manner allowing their performance to be ranked. It is important to note that for the best algorithm, wRMSD = RMSDe as the best algorithm will always have a score of 1. To ensure the robustness of any statistics generated it was considered prudent to specify a minimum data threshold.
Algorithm evaluation A multipurpose global reference matchups database (MDB) matching in situ carbonate system parameters from the surface 10 m with satellite, model and interpolated in situ datasets was used to perform the algorithm evaluation  (Land et al. , 2022). (Boutin et al. , 2020; Boutin et al. , 2021 ), CORA v5. 2 (Szekely et al. , 2019) , the in situ Analysis System (ISAS 15 )  (Kolodziejczyk et al. , 2021; Gaillard et al. , 2016)  and Remote Sensing Systems data from the Soil Moisture Active Passive satellite (RSS-SMAP) level 3 v4. 0  (Meissner et al. , 2019; Meissner et al. , 2018) ). The MDB also contains TA, DIC and pH values, most of which come from the Global Ocean Data Analysis Project (GLODAPv2. 2020) database  (Olsen et al. , 2016)  and pCO 2 values most of which come from SOCAT v2020  (Bakker et al.
, 2016)  along with additional data for Arctic waters. A full list of references for data variable sources used in the MDB can be found in  (Land et al. , 2022). Before beginning this analysis we limited our analysis to only used MDB data that fall within these bounds: SST>-10 °C or <40 °C, SSS>0 or <50, DIC>500 μmol kg -1 or <3000 μmol kg -1 , TA>500 μmol kg -1 or <3000 μmol kg -1 , pH >6 or <8. 5 and pCO 2 >100 μatm or <3000 μatm and these constraints are consistent with the conditions likely within the Amazon and Congo regions. The Each valid algorithm was implemented by closely following the specific literature recommendations; this was done in such a way that empirical algorithms were only assessed for the geographical and geophysical ranges for which they were originally developed. The algorithm evaluation involved running each algorithm with inputs from the MDB to estimate the TA and DIC outputs (termed the "algorithm output").
Each algorithm output was then evaluated using the TA and DIC values from the MDB matchup database (termed the "reference output"). The RMSD, bias, correlation coefficient, and MAD are calculated from the algorithm output and the reference output. Additionally, wRMSD was calculated for algorithms where an RMSD is reported in the literature. Where wRMSD was calculated and the matchups (n) were >30, RMSDe was calculated for those algorithms following the methodology of  Land et al. (2019). As the MDB includes three global SST and four global SSS datasets, each algorithm was run for each of the twelve combinations of SST and SSS input datasets, and separate statistics were generated for each configuration. The subset of input variables from the MDB used to implement the algorithm, the algorithm output and the reference output for all 12 SST and SSS combinations are included in the supplementary materials.
Additionally, summary statistics between the algorithm output and the reference output, (mean, wmean, standard deviation, RMSD, wRMSD, RMSDe, Bias, wBias, r, wr, MAD and wMAD) are also provided in the supplementary materials. The best performing TA and DIC algorithms for both regions were determined by ranking the algorithms by RMSDe, the best performing algorithms were the algorithms with the lowest RMSDe values. In order to select algorithms suitable for generating time series, observation datasets for SSS and SST needed a temporal overlap of at least 8 years. The remainder of this paper exclusively discusses the time series generated with these "optimal algorithms". The algorithm evaluation process follows a standardised framework for a Type A uncertainty evaluation for TA and DIC whereby the following sources of uncertainties are considered: 1.
, 2022)). By accounting for all known sources of uncertainty this constitutes a complete Type A uncertainty evaluation. The combined standard uncertainty (δQ) for TA and DIC is calculated as δQ = ((δa) 2 + (δb) 2 ) 1/2 where δa is the RMSDe from the evaluation algorithm and δb is the wBias. The uncertainty estimates provided within the dataset is the combined standard uncertainty (δQ) and these combined standard uncertainties are reported without confidence intervals. To aid interpretation of the combined uncertainty budgets, a second Type A uncertainty evaluation of the TA and DIC approaches was calculated based purely on the empirical algorithm (e. g. literature RMSD value) and the uncertainties in the input data propagated using standard techniques  (Taylor, 1997)  and assuming that any uncertainties were uncorrelated.
Creation of gridded monthly time series product The full spatiotemporal resolution SST and SSS datasets and gridded World Ocean Atlas (WOA) DO, NO 3 -, PO 4 -and SiO 4 datasets were downloaded from their online repositories, the total size of these full resolution datasets occupies ~500 GB of hard drive space and take several days to fully download. The SST and SSS datasets were uncompressed and were regridded onto a monthly 1° X 1° standard World Geodetic System grid for use creating the gridded output, the grid has 180° of Latitude and 360° of Longitude with 0° centred on the Greenwich Meridian. Reformatting the datasets considerably reduced the file size to a more manageable ~50 GB. The scripts used to download these SST and SSS monthly datasets are fully automated, meaning that when new data are added to these datasets it can be easily incorporated into future versions of OceanSODA-UNEXE.
For each target variables (TA or DIC), the selected optimal SST and SSS gridded datasets and gridded WOA datasets were used as inputs to the respective TA and DIC algorithms. The algorithms were applied to the datasets in the two outflow regions where the SST and SSS dataset overlapped in time for the published environmental limits of each algorithm. The monthly SST and SSS inputs as well as the calculated monthly output variable (TA or DIC) are saved to a netCDF4 file as separate variables on the grid described above with dimensions of 768 by 180 by 360 (where 768 is the time step in months starting in January 1957). It is important to note that TA and DIC datasets (even if for the same region) can span different temporal domains and so should be treated as separate datasets. These differences are due to differences in the temporal range of the input data used to calculate them.
Where there was temporal overlap between the TA and DIC datasets in each region, the pyCO2SYS v1. 7. 1 software package  (Humphreys et al. , 2022)  was used to calculate the complete carbonate system variables (pCO 2 , fCO 2 and pH on the free, total and seawater scales), the carbonate ion content (CO 3 -2 ), the bicarbonate ion content (HCO 3 -), the hydrogen ion content with the carbonic acid dissociation constants of  Mehrbach (1973)  refitted by  Dickson and Millero (1987)  and the hydrogen sulphate dissociation constant of  Dickson (1990). The full carbonate system is calculated twice, once using the optimal SST and SSS datasets that were used to calculate TA then again using those used to calculate DIC. Consequently each netCDF file contains a complete set of carbonate system outputs that are unique to that input dataset.
Whilst some of the SST and SSS products used to create the gridded datasets contain only satellite observed SST at a specified depth, some of the re-analysis SST and SSS products also incorporate surface ocean measurements from ships and buoys which were made at differing depths. The TA and DIC data used to generate the MDB comes from the top 10 m of the ocean and has not been adjusted for potential concentrations gradients in the surface ocean  (Land et al. , 2022). For these reasons, OceanSODA-UNEXE is deemed relevant in the surface 10 m of the ocean.
Uncertainties in gridded monthly time series product As stated in section 2. 3, the assessed combined standard uncertainties for TA and DIC are single fixed values provided as regionally-static (non-spatially varying) fields in the netCDF files. Uncertainty information for SST and SSS are taken from the literature references and are provided as spatially varying fields in the netCDF files (as provided in the original datasets). Uncertainties in TA, DIC, SST and SSS are provided as optional uncertainty input arguments in pyCO2SYS to generate uncertainties for pH, pCO 2 , Ω Calcite and Ω Aragonite. PyCO2SYS uses a forward finite difference approach to calculate the derivatives needed to propagate uncertainties for all these output variables. This produces a spatially varying combined uncertainty budget for each remaining carbonate system parameter.
Algorithm evaluation The algorithm evaluation RMSDe values in the Amazon and Congo for TA and DIC are shown in Figure  1. From these RMSDe values we selected the "optimal algorithms" for both regions as the algorithm combination with the lowest RMSDe. The optimal algorithms were then used to produce OceanSODA-UNEXE. In the Amazon the optimal algorithm to generate the TA data was  (Cai et al. , 2010)  with ESACCI SST and ESACCI SSS (Table  1 ). To generate DIC for the Amazon the best algorithm was  (Lefèvre et al. , 2010)  using OISST SST and ESACCI SSS. In the Congo, the optimal algorithm to generate the TA data was  Lee et al. (2006)  with CORA SST and ISAS SSS (Table  2 ). For DIC in the Congo the optimal algorithm was  (Lee et al. , 2000)  with ESACCI SST and CORA SSS. Table  1 : Summary table of the best combination of algorithm and SST/SSS datasets (aka the "optimal algorithms") in the Amazon River domain.
Amazon dataset The primary output datasets are the monthly gridded carbonate system variables (DIC and TA). The seasonal progression of both these variables is shown in the Amazon outflow region in Figures  2  and 3. There is a large range of DIC and TA in this (Figure  2b  and 3b), a feature reported elsewhere  (Chérubin and Richardson, 2007). From July to September (Figure  2c  and 3c) another large outflow region occurs just off the coast of Venezuela at the mouth of the Orinoco River  (Hu et al. , 2004). The Orinoco plume is much less prominent in the other seasons. The proximity of the Orinoco River to the Caribbean suggests that the Orinoco plume often reaches the islands between July and September  (López et al. , 2013). There are some differences between the structure of the average TA and DIC plumes which may indicate active processes in the plume, for example gas exchange which only affects DIC or biological production which affects TA and DIC at different rates.
Annual and seasonal data averages over the region shown in Figures  2  and 3  are provided in Table  S3  for DIC, TA, pH, pCO 2 , Ω Calcite and Ω Aragonite. The temporal aspect of the dataset is presented in Figure  4  as a meridional Hovmöller plot at 52° W (marked on Figures  2  and 3 ) which broadly cuts through the central plume outflow at 4° N, the mixing of the plume up to 10° N and the open ocean Atlantic water >10° N. The lowest DIC values (~1400 μmol kg-1) are found in the plume at 4° N, there is regular seasonality with lower values in the October to December and higher values in April to June (Figure  4a ). There does not appear to be much interannual variability in the magnitude of the DIC plume in the June to August period, although 2010 and 2013 appear to be weaker (Figure  4a ).