dataesr/scientific-paragraphs-categorization huggingface dataset:
Validation data and methodology In this study, we have tended to use the same evaluation data sets as during the MACC near-real-time validation exercise. This implies some discontinuities in the evaluations, e. g. the substitution of SCIAMACHY data with GOME-2 data after  MACCity / MEGAN / GFASv1. 0 daily the loss of the Envisat sensor or an exclusion of MOPITT satellite data after the start of its assimilation into the model. The continuous process of updating and complementation of data sets in databases requires the selection and definition of a validation data set at some point. The comparatively small inconsistencies between our data sets are considered to have a negligible impact on the overall evaluation results.
Hourly O 3 ,CO and NO 2 data have been downloaded from the WMO/GAW World Data Centre for Greenhouse Gases (WDCGG) for the period between September 2009 and December 2012 (the download was carried out in July 2013). Our validation includes 6 stations with surface observations for NO 2 , 29 stations for CO and 50 stations with surface ob-servations for O 3. Table  3  lists the geographic coordinates and altitudes of the individual stations. Being a long-term data network, the data in the database are provided with a temporal delay of approximately 2 years. As the data in the database become sparse towards the end of the validation period, near-real-time observations, as used in the MACCproject for near-real-time validation, presented on the MACC website, have been included to complement the validation data sets.
For the detection of long-term trends and year-toyear variability, the data quality objectives (DQOs) for CO in GAW measurements are set to a maximum uncertainty of ±2 ppb and to ±5 ppb for marine boundary layer sites and continental sites that are influenced by regional pollution, and to ±1 ppb for ozone  (WMO, 2012 (WMO, , 2013) )  and 0. 08 ppb for NO 2  (WMO, 2011). For the validation with GAW station data, 6-hourly values (00:00, 06:00, 12:00, 18:00 UTC) of the analysis mode have been extracted from the model and are matched with hourly observational GAW station data. Model mixing ratios at the stations' locations have been linearly interpolated from the model data in the horizontal. In the vertical, modelled gas mixing ratios have been extracted at the model level, which is closest to the GAW stations' altitude.
EMEP surface O 3 observations The EMEP is a scientifically based and policy driven programme under the Convention on Long-Range Transboundary Air Pollution (CLRTAP) for international co-operation to solve transboundary air pollution problems. Measurements of air quality in Europe have been carried out under the EMEP programme since 1977. A detailed description of the EMEP measurement programme can be found in  Tørseth et al. (2012). The surface hourly ozone data between September 2009 and December 2012 have been downloaded from the EMEP data web page (http://www. nilu. no/projects/ccc/emepdata. html). For the validation, only stations meeting the 75 % availability threshold per day and per month are taken into account. The precision is close to 1. 5 ppb for a 10 s measurement. More information about the ozone data quality, calibration and maintenance procedures can be found in  Aas et al.
(2000). For comparison with EMEP data, 3-hourly model values (00:00, 03:00, 06:00, 12:00, 15:00, 18:00, 21:00 UTC) of the analysis mode have been chosen. We used this data set to test the dependency of the biases on a daytime and nighttime basis, separately. Gas mixing ratios have been extracted from the model and are matched with hourly observational surface ozone data at 124 EMEP stations in the same way as for the GAW station data. The EMEP surface ozone values and the interpolated surface modelled values are compared on a monthly basis for the latitude bands of 30-40 • N (southern Europe), 40-50 • N (central Europe) and 50-70 • N (northern Europe). For the identification of differences in the MACC_osuite performance between day and night-time, the MACC_osuite simulations and the EMEP observations for the three latitude bands have been additionally separated into daytime (12:00-15:00 local time, LT) and night-time (00:00-03:00 LT) intervals.
(1) Details on the method of calculation are referred to in  Deeter et al. (2004)  and  Rodgers (2000). The averaging kernels indicate the sensitivity of the MOPITT measurement and retrieval system to the true CO profile, with the remainder of the information set by the a priori profile and retrieval constraints  (Emmons, 2009; Deeter et al. , 2010). The CO data X * (derived using the above equation) have the same vertical resolution and a priori dependence as the MOPITT retrievals and have been used to calculate averaging kernel smoothed model CO total columns, which are compared to the MO-PITT CO total columns. For the validation, eight regions are defined (see Fig. 1 ): Europe, Alaska, Siberia, North Africa, southern Africa, South Asia, East Asia and the United States. The model update in July 2012 includes an integration of MOPITT CO total columns in the model's data assimilation system.
This most likely leads to some minor overestimation of model NO 2 VCDs compared to GOME-2 data, as the Equator crossing time for GOME-2 is about 09:30 LT. Moreover, only model data for which corresponding satellite observations exist are considered. For the validation, the same regions have been used as for MOPITT (Fig. 1 ), except for Siberia and Alaska. In contrast to comparisons of MOPITT and model data of CO, no averaging kernels were applied to the model NO 2 data. Satellite observations of tropospheric NO 2 columns have relatively large uncertainties, mainly linked to errors in the stratospheric correction method, i. e. in stratospheric NO 2 columns (important over clean regions and at high latitudes in winter and spring) and to uncertainties in air mass factors (mainly over polluted regions) (e.
Correlation coefficients from the comparison with GAW station data calculated over the whole time period range between 0 and 0. 8 (on average 0. 4), with only four stations showing values smaller than 0. 2 (RIG, BEO, East Trout Lake (ETL) and Lac la Biche (LAC); the latter two located in Canada). Considering the global monthly MNMBs and RMSEs, it can be seen that during the northern hemispheric summer months, June to September, both are small (absolute differences less than 5 %); see Figs. 10  and 11. Negative MN-MBs (up to -35 %) and larger RMSEs (up to 72 ppb) appear during the northern hemispheric winter months, November to March, when anthropogenic emissions are at a highest level, especially for the US, northern latitudes and Europe. Monthly correlation coefficients are between 0. 1 and 0.
9 ), show that the drop of CO, associated with the air mass change from continental to cleaner marine air masses after the onset of the monsoon season during the early summer months, is captured by the MACC_osuite. Deterioration in all scores is visible during December 2010 in the time series plots of several stations (e. g. Jungfraujoch (JFJ), and Sonnblick (SBL), Fig. 9 ). This is likely a result of changes in   -7. 28 -10. 80 -11. 85 -14. 79 -13. 50 -14. 16 -15. 30 -11. 49 -7. 00 -3. 65 Alaska -5. the processing of the L2 IASI data and a temporary blacklisting of IASI data (to avoid model failure) in the assimilation. The comparison with MOPITT satellite CO total columns between October 2009 and June 2012 (described in Sect. 2. 2. 3) shows a good qualitative agreement of spatial patterns and seasonality; see Table  6.
14 ). However, larger differences between MOPITT and IASI data appear during the north-ern winter months over Alaska, Siberia, Europe and the US, which result in lower CO concentrations in the model, due to the assimilation of IASI CO data in the MACC_osuite. The differences between MOPITT and IASI data can be mainly explained by the use of different a priori assumptions in the IASI and MOPITT retrieval algorithms  (George et al. , 2015). The Fast Optimal Retrievals on Layers for IASI (FORLI;  Hurtmans et al. , 2012)  software uses a single a priori CO profile (with an associated variance-covariance matrix) whereas the MOPITT retrieval algorithm uses a variable a priori, depending on time and location. George et al. (2015)  showed that differences above Europe and the US in January and December (for a 6 year study) decrease by a factor of 2 when comparing IASI with a modified MOPITT product using the IASI single a priori.
e. 2000. For CO, uncertainties in the evaluation also include the retrieved amount of CO total columns between IASI and MOPITT. These vary with region, with IASI showing lower CO concentrations in several regions (Alaska, Siberia, Europe and the US) during the northern winter months, which possibly contribute to the deviations observed between the modelled data and MOPITT satellite data, as only IASI data have been assimilated in the model. The differences can primarily be explained by the use of different a priori assumptions in the IASI and MOPITT retrieval algorithms  (George et al. , 2015). On a global scale, however, the average difference between the IASI and MOPITT total columns is less than 10 %  (George et al. , 2009). Modelled NO 2 tropospheric columns agree well with satellite observations over the US, South Asia and North Africa.
Conclusion The MACC_osuite is the global near-real-time MACC model analysis run for aerosol and reactive gases. The model has been evaluated with surface observations and satellite data concerning its ability to simulate reactive gases in the troposphere. Results showed that the model proved capable of a realistic reproduction of the observed annual cycle for CO, NO 2 and O 3 mixing ratios at the surface, however, with seasonally dependent biases. For ozone, these seasonal biases likely result from difficulties in the simulation of vertical mixing at night and deficiencies in the model's dry deposition parameterisation. For CO, a negative offset in the model during the winter season is attributed to limitations in the emission inventories together with an insufficiently established seasonality in the emissions.
The NO 2 total columns derived from satellite sensors and surface NO 2 observed by European GAW stations could be reproduced reasonably well over most of the evaluated regions, but showed a negative offset compared to the observational data, especially over Europe and East Asia. It has become clear, that the emission inventories play a crucial role in the quality of model results and remain a challenge for near-real-time modelling, especially over regions with rapid changes in emissions. Inconsistencies in the assimilated satellite data and fire emissions showed only a temporary impact on the quality of model results. The implementation of a model update improved the results especially at high latitudes (surface ozone) and over South and East Asia. The MACC NRT forecast system is constantly evolving.
Acknowledgements. This work has been carried out in the framework of the MACC projects, funded under the  EU Seventh Research Framework Programme  for research and technological development. The authors thank the MACC validation and reactive gas subproject teams for fruitful discussions. Model simulations were carried out using the ECMWF supercomputer. We wish to acknowledge the provision of GAW hourly station data from the  World Data Centre of Greenhouse Gases (WDCGG)  and hourly EMEP station data from the  Norwegian Institute for Air Research (NILU ) database. Specifically, we like to thank the  Commonwealth Scientific and Industrial Research Organisation (CSIRO) Oceans  and  Atmosphere Flagship  for making the data freely available and the  Australian Bureau of Meteorology  for continued operation and support of the Cape Grim station.
Volumetrically, turbidity currents are the prime suppliers of sediment to the deep sea, and conveyors of organic carbon from the terrestrial biosphere and submarine shelf into marine depositional basins. They result from complex processes of erosion, transport and deposition that can be difficult to study in detail. Here we present data from the Gaoping submarine canyon system, off SW Taiwan, which was perturbed in 2009 by the addition of flood deposits following Typhoon Morakot and sampled by gravity coring less than 2 months after the event. We use the different origins of organic carbon, distinguished by their carbon and nitrogen concentrations and δ 13 C and δ 15 N isotopic composition, to compare and contrast standard and extreme sedimentological conditions. Using well-constrained end-members, the results were de-convolved into inputs of metamorphic and sedimentary fossil organic carbon eroded within the Gaoping River basin, terrestrial non-fossil carbon and marine organic matter.
Core L9 measurements plot accordingly, whereas the data from core K11A does not (Fig. 4a ). It has a diminished plant biomass contribution Fig. 5. Depth-averaged results of endmember unmixing for each core. Black bars represent the contribution of terrestrial biomass (both modern plant material and recycled low-grade sedimentary carbon), light grey bars represent bedrock fossil carbon and mid-grey bars represent marine biomass. Cores are arranged in a proximal to distal order. Suspended sediment samples from the Gaoping River are also shown. These were collected prior to Morakot  (Hilton et al. , 2010)  and represent an alternative sedimentary routing mechanism dominated by soil erosion and suspended load transport rather than routing of woody debris. Error bars corresponding to estimated uncertainty of ±0.
29 wt. % C and a depth-averaged OC biosphere + OC petro-sed concentration of 0. 15 wt. % C (Fig. 5 ). This composition contrasts with that of Gaoping River bedload samples, which were collected from overbank deposits and partially reworked fluvial sand bars two months after the storm. Whilst the OC petro-meta content was similar between these settings, 0. 17 ± 0. 06 wt. % C, the OC biosphere + OC petro-sed component of the river bedload sediments, 0. 08 ± 0. 03 wt. % C, was only half that of the cores. The organic matter content of these cores is closer to that of river suspended load. The average amount of POC in the Gaoping River suspended load classed as OC petro-meta was 0. 48 ± 0. 20 wt. % C, whilst OC biosphere + OC petro-sed was 0. 12 ± 0. 09 wt. % C, based on data from  Hilton et al.
Acknowledgements RS was supported by an  Engineering and Physical Sciences Research Council  ( EP/P502365/1  and  EP/P504120/1 ) studentship. JTL was supported by grant number  NSC 95-2745-M-110-001  for the  Fate of Terrestrial-Nonterrestrial Sediments in High Yield Particle-Export River-Sea Systems Program , which provided the cores in this study. We thank  Peter Talling  for his insightful and constructive comments on the manuscript and a further, anonymous reviewer for generous endorsement.
As illustrated in Fig. 9d  the averaging kernels for a representative case (in red in Fig. 9a-c ) show a non-zero sensitivity at the surface for MOPITT. Another possible explanation lies in the constraint applied to the measurements (the S e in the OE), which might be looser in FORLI, increasing further the range of variability. Looking at the total errors associated with each retrieved profiles (Fig. 9e ), we note that the three profiles are within the errors of each other, which indicates the consistency of the data sets. The errors in % are plotted in Fig. 9f. The MOPITT vX1 total error profile is close to the IASI one because the smoothing error dominates. In order to go further in the analysis we selected three illustrative cases, representative of different situations, for which aircraft profile data from the MOZAIC-IAGOS program  (Nedelec et al.
Acknowledgements. The French scientists are very grateful to  NCAR  and its visitor program, which allowed the fruitful scientific collaboration between the IASI and MOPITT teams to develop and to be maintained for years. NCAR is sponsored by the  National Science Foundation. IASI is a joint mission of  EUMETSAT  and the  Centre National d'Etudes Spatiales (CNES, France). The  IASI L1  and  L2  input data are distributed in near real time by  EUMETSAT  through the EumetCast system distribution. The  MOPITT  project is supported by the  NASA   Earth Observing System (EOS) Program. The MOPITT team also acknowledges support under  NASA  grant  NNX11AE19G. The authors acknowledge the  European Commission  for the support to the  MOZAIC  project (1994)(1995)(1996)(1997)(1998)(1999)(2000)(2001)(2002)(2003) and the preparatory phase of IAGOS (2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012).
The LATMOS team also acknowledges the  French Ether atmospheric database  (www. pole-ether. fr)for providing the IASI L1C data and L2 temperature data disseminated via EUMETcast, as well as  CNES  and  CNRS  for financial support. This work is also part of the EUMETSAT/ O3M-SAF  project. The research in Belgium is funded by the  Belgian State Federal Office for Scientific, Technical and Cultural Affairs  and the  European Space Agency (ESA Prodex arrangement 4000111403 IASI. Flow) and by the  EU-FP7  projects  QA4ECV  (grant agreement  607405 ) and  PANDA  (grant agreement  606719 ). P. -F. Coheur is Senior Research Associate with F. R. S-FNRS. Edited by:  J. -L. Attie.
minimus  (Moszczynska et al. 2009 ) and a newly developed primer pair for Gymnophallus choledochus, namely FdigF (5'-TTIITTWCGTTRGATCATAAGC-3') and FdigR (5'-GAAAGMAGAAYCAAAATTACGATC-3'). Development of primers FdigF and FdigR was done using the program Primer3  (Rozen & Skaletsky 2000)  and based on COI sequences of B. minimus and eleven other digenean species (Genbank accession numbers NC0111272, NC0121472, NC0025461, NC0025441, NC0023542, NC0096801, NC0025291, NC0080741, NC0080671, NC0025451, EU8765281). PCR for B. minimus was performed in 25-µl reaction volumes containing 1 X PCR buffer, 0. 25 mM of each dNTP, 0. 5 µM primer MplatCOX1-dF, 0. 5 µM primer MplatCOX1-dR, 0. 025 units Biotherm plus DNA polymerase and 1 µl undiluted genomic DNA. Each 25-µl PCR reaction for G. choledochus contained 1 X buffer, 0.
choledochus, G. australis, Meiogymnophallus minutus and Bartolus pierrei (Genbank accession numbers JN381027-JN381030;  Pina et al. , unpublished). PCR was performed in 20-µl reaction volumes containing 1 X PCR buffer, 0. 25 mM of each dNTP, 1 µM primer GITS2f, 1 µM primer GITS2r, 0. 40 µM BSA, 0. 005 units Biotherm plus DNA polymerase and 2 µl undiluted genomic DNA extract. For the amplification, Doppio thermocyclers were used with the following temperature cycling profile: 95°C for 5 min, followed by 35 cycles of 95°C for 60 s, annealing at 52°C for Amplifications were checked with 2% agarose gel electrophoresis. PCR product purification and cycle sequencing was performed at Macrogen Inc. (Seoul, South Korea) on ABI3730 automated sequencers.
Data analyses Sequences were aligned by hand using the program BioEdit  (Hall 1999). COI sequences of B. minimus were cropped to 587 bp and those of G. choledochus to 527 bp. ITS2 sequences for G. choledochus were cropped to 296 bp with flanking regions of 105 bp for the 5. 8S gene downstream and 34 bp of the 28S gene upstream. The taxon identity of the sequences was confirmed to be most similar to other digenean trematodes based on a Genbank's BLAST (http://blast. ncbi. nlm. nih. gov). Amino acid translations were examined on the basis of the echinoderm and flatworm mitochondrial genetic code. Minimum spanning networks among haplotypes were estimated using Arlequin 3. 5  (Excoffier et al. 2010). Partitioning of molecular variance among alternative population groupings was estimated using analysis of molecular variance (AMOVA) in Arlequin 3.
g. ,  Olson et al. 2009 ) was used and a generation time of one year was assumed. Fixed-rate analyses were run using a strict molecular clock and maximum likelihood substitution model as determined using the software MEGA5. 2. 2  (Tamura et al. 2011) , for a chain length of 1 x 10 7 generations and a burnin time of 10%. Graphical EBSP reconstructions were generated in Tracer v1. 5  (Rambaut & Drummond 2007).
Sequence variation From 18,059 cockles dissected, 166 B. minimus and 110 G. choledochus samples were taken. Infection percentages in adult cockles ranged from 0. 00% to 5. 34% for B. minimus and from 0. 05% to 10. 1% for G. choledochus (Table  1 ). Counting of infections ceased when 30 individuals were collected at a location. A total of 135 B. minimus and 90 G. choledochus were successfully sequenced for cytochrome c oxidase 1, resulting in the detection of 54 and 36 haplotypes, respectively (Genbank accession numbers KF880428 -KF880481 and KF880482 -KF880517, Tables  2  and 3 ). The minimum spanning network of G. choledochus COI haplotypes (shown in Fig. 3A ) was relatively simple with a central, very abundant, haplotype and most variants within a single mutational step. The star-like structure of the network suggests recent population expansion.
The presence of long branches in G. choledochus could point to cryptic species. To examine the possibility of cryptic species, we sequenced a representative set of 14 individuals for the ITS2 (internal transcribed spacer 2) and portions of the flanking regions of the ribosomal genes 5. 8S and 28S (Genbank accession numbers KF880518 -KF880531). The length of ITS2 was 296 bp in all haplotypes; the portion of 5. 8 S sequenced was 105 bp and the portion of 28 S was 34 bp. All sequence portions for 5. 8S and 28S were identical to the G. choledochus rDNA entry in Genbank (JN381029, sampled in Portugal from Cerastoderma edule) as well as to the G. australis entry (JN381028, sampled in Argentina from Perumytilus purpuratus) (Pina, Cremonte & Rodrigues, unpublished). The minimum spanning network among the six ITS2 haplotypes detected is shown in Fig.
4. The most common ITS2 haplotype was identical to the Genbank entry for G. choledochus from Portugal, while there were five gaps plus a 21% sequence difference with respect to ITS2 in G. australis from Argentina. Comparative COI and ITS2 divergences among trematode species pairs were investigated by  Vilas et al. (2005). They found differences of as little as 6. 3% for COI and 0. 3% for ITS2. Given our much higher divergences of 15. 3% and 0. 68%, respectively, it is probable that at least this haplotype may represent a cryptic species. Because pseudogenes (although unlikely) and cryptic speciation may underlie the long branches in G. choledochus COI, we carried out all population and species level comparisons both with and without a long branch subset. The long branch subset consisted of all haplotypes six or more replacement substitutions away from the main haplotype: GyA, GyB, GyX, GyG, GyC and GyO.
This decreased the number of individuals in the G. choledochus COI data set by N=11. The demographic analyses were done with groupings based on the AMOVA and pairwise comparison results. Hence, all samples for G. choledochus were grouped and analysed together, and all samples for B. minimus were analysed separately. Mismatch distributions are shown in Figure  S1. The RI statistic rejected a model of sudden population expansion in none of the seven data sets analysed (Table  5 ). The SSD statistic rejected the null hypothesis of significant population expansion model in two out of seven cases. In G. choledochus, a model of expansion was rejected only in case the 'replacement set' of haplotypes was included in the analysis (P<0. 005, Table  5 ). In B. minimus, the expansion model was rejected for the sample originating from the Celtic Sea (P<0.