dataesr/scientific-paragraphs-categorization huggingface dataset:
Finally, the population genetic structure was explored with the software STRUCTURE v. 2. 3  49  , a Bayesian clustering method that assigns individuals to groups (k) without prior information. The analysis was run under the admixture model  49  with K ranging from 1 to 9 (n° of sampled populations plus three) and 20 independent runs with a burn-in of 100,000 iterations and a run-length of 500,000 iterations. The most likely K (number of groups) was inferred using the DeltaK criterion  50  with STRUCTURE HARVESTER  51. Juveniles were assigned to adult populations using GENECLASS v. 2. 0  52  under the Bayesian assignment method  53. Adult genotypes of each of the six sites were used as reference populations and juveniles (obtained solely in Mallorca and Banyuls) were then either assigned or excluded from each of the populations using the Monte Carlo re-sampling approach (n: 10,000)  54.
Mitochondrial DNA. Genetic diversity. The 590 base pair (bp) fragment of COI was aligned with available Genbank sequences, obtaining a 243 bp sequence alignment for 292 P. nobilis individuals from 24 sites in the Mediterranean, defined by 32 polymorphic sites comprising a total of 43 haplotypes (Table  S3 ). Most haplotypes (61%) were found only at a single locality, as 17 were shared haplotypes and 23 were singletons. The highest values of haplotype and nucleotide diversity were found in the populations of: Alicante, Elba Island, Corsica (Isola plana), the Venetian Lagoon and Sicily (Milazzo). The lowest values of haplotype and nucleotide diversity were found in Murcia, the Ebro Delta, Aggeloyesori in the Aegean Sea and el Ketef in the Tunisian population (Table  S3 ). The haplotype sequences were registered in Genbank (accession n°: KY321755-KY321811).
All locations, except Murcia, presented exclusive haplotypes (Table  1 ). The majority of the singletons occupied a distal position in the network, an indication of more recent haplotypes in accordance with the criteria of  58. The mismatch analysis of both analyses of COI, 1) 24 populations from the whole Mediterranean basin; 2) 6 Western Mediterranean populations, revealed an unimodal distribution (  59, 60  ; Fig. S1a  & S1b ). The dataset exhibited negative and significant Tajima's D and Fu's FS values, suggesting a sudden population expansion in the Ebro Delta, Ibiza and Mallorca (Table  S6  and S7 ). However, the Ramos-Onsins' R2 test, which is more powerful than D and F ST at small sample sizes, only corroborated this sudden population expansion in Ibiza and Mallorca (Table  S6 ) and also detected it in the populations of Alicante and Aggeloyesori (Table  S6  and S7 ).
The correspondence analysis based on the allele frequencies (58. 6% of the total variance for the two first ordination axes) revealed a weak geographic structure showing three major groups: (1) Banyuls on the positive part of the axis I; the axis II separated (2) The Ebro Delta and the Balearic localities (Ibiza and Mallorca) on the positive site and (3) Alicante and Murcia on the negative site (Fig. S2 ). The STRUCTURE analysis, using the 48 method for estimating the true K, indicated also that there are three clusters in the data set. Banyuls exhibited a higher likelihood of belonging to cluster 1, Mallorca to cluster 2 and Murcia to cluster 3. By contrast, the individuals from the Ebro Delta, Alicante and Ibiza were not assigned predominantly to either one of these three clusters, showing a strong ad-mixture (Table  3 ).
Acknowledgements This is a contribution to project  MEDEICG  funded by the  Spanish Ministry of Economy and Competitiveness  ( CTM2009-07013 ). IEH was supported by  Ramon y Cajal Fellowship   RYC2014-14970 , cofunded by the  Conselleria d'Innovació, Recerca i Turisme of the Balearic Government (Pla de ciència , tecnologia, innovació i emprenedoria  2013-2017 ) and the  Spanish Ministry of Economy, Industry and Competitiveness IFCT Investigator Programme-Career Development  ( IF/00998/2014 ) supported MGW and AHE was supported by  FCT  fellowships  SFRH/BPD/63703/2009  and  SFRH/BPD/107878/2015. Lionel Renault appreciates support from the  National Science Foundation  ( OCE-1419450 ) and  Ester Serrao  thanks the support of  FCT  for their CCMAR team via excellence research line  EXCL/AAG-GLO/0661/2012.
A 'N' corresponds either to a low-quality sequence (including the possibility of a gap, especially when the same nucleotide occurred at consecutive positions) or a putative polymorphism of rDNA repeats. The sequences are available at Genbank with accession # (TO BE SUBMITTED). The sequences were run through NCBI Blast (April-May 2018) with default parameters. In case of the apparent polymorphism in our Panagrellus redivivoides 28S sequences (AGTTGATCGGGTGTTGGCTTCGGY; cf. S2B Table, column P), the highest peak was used in the blast analysis. Differences with the closest sequence in the database were manually checked.
Among them, we found P. pacificus, as verified by crosses (S2 Table ). Some of our 18S sequences do not match those of any Pristionchus species in the databases and may be new species. Conversely, some Pristionchus species may not be distinguished by this short fragment. Importantly, the sampled diversity covers all groups of Pristionchus species in  [56] , except the more basal 'Elegans' group. Indeed, we found 18S best hits in the Pacificus group to P. pacificus, P. arcanus, P. japonicus and P. quartusdecimus, in the Maupasi group to P. atlanticus, in the Lheritieri group to P. uniformis and P. entomophagus, and in the Triformis group to P. triformis and P. hoplostomus. In addition, in a rotting bulb we found a putative representative of the fresh fig clade  [15]  with a sequence resembling that of Pristionchus sp.
Description of the AnMBR plant The data used in this study to calibrate and validate the filtration model was obtained from an AnMBR that mainly consisted of an anaerobic reactor with a working volume of 0. 9 m 3 connected to two membrane tanks. Each membrane tank had a working volume of 0. 6 m 3 and included one ultrafiltration hollow-fiber membrane commercial system (PURON ® , Koch Membrane Systems, 0. 03 µm pore size, 31 m 2 total filtering area and outside-in filtration). The plant was fully automated and monitored online in real-time. In addition, the anaerobic sludge was sampled once a day to assess the filtration performance. The concentration of mixed liquor total solids (MLTS) was determined according to the Standard Methods  (APHA, 2005). A more precise description of the plant and its instrumentation (as well as the corresponding flow diagrams) can be found elsewhere  (Robles et al.
Characteristics of the substrates As aforementioned, the proposed model-based optimization strategy was validated for an AnMBR treating UWW and a mixture of UWW and FW. To this aim, a filtration model was calibrated and validated using data from an AnMBR system that treated UWW and a mixture of UWW and FW. The UWW was the effluent from the pre-treatment step of the Carraixet WWTP (Valencia, Spain) and the FW was collected from canteens in the university  (Moñino et al. , 2016). The UWW was characterized by a low COD/SO 4 -S ratio and the mixture of UWW and FW was set to different penetration factors (PF, defined as the percentage of the population having a kitchen disposer). The COD/SO 4 -S ratio of the UWW was around 6. 6 kg COD•kg -1 SO 4 -S while the COD/SO 4 -S ratio of the UWW was around 9.
Simulations for real-time dynamic optimization of the filtration process The dynamic optimization of the filtration process was carried out using the costs equation (Eq. 4) as objective function. The optimization algorithm was applied by using the trust region approach  (Coleman and Li, 1996) , based on the Newton method (LSQNONLIN function in MATLAB ® ) and the Runge-Kutta method (ode45 function in MATLAB ® ).
Acknowledgements This research work was possible thanks to financial support from  Generalitat Valenciana  (project  PROMETEO/2012/029 ) which is gratefully acknowledged. Besides, support from  FCC  Aqualia participation in INNPRONTA 2011 IISIS IPT-20111023 project (partially funded by The  Centre for Industrial Technological Development (CDTI)  and from the  Spanish Ministry of Economy and Competitiveness)  is gratefully acknowledged.
This study investigates the role of biomass burning and long-range transport in the anomalies of carbon monoxide (CO) regularly observed along the tropospheric vertical profiles measured in the framework of IAGOS. Considering the high interannual variability of biomass burning emissions and the episodic nature of pollution longrange transport, one strength of this study is the amount of data taken into account, namely 30,000 vertical profiles at 9 clusters of airports in Europe, North America, Asia, India and southern Africa over the period 2002-2017. As a preliminary, a brief overview of the spatio-temporal variability, latitudinal distribution, interannual variability and trends of biomass burning CO emissions from 14 regions is provided. The distribution of CO mixing ratios at different levels of the troposphere is also provided based on the entire IAGOS database (125 million CO observations).
In both Japan and south India, the anthropogenic emissions dominate all along the year, except for the strongest summertime anomalies observed in Japan that are due to Siberian fires. The strongest CO anomalies at airport clusters located in south-east Asia are induced by fires burning during spring in south-east Asia and during fall in equatorial Asia. In southern Africa, the Windhoek airport was mainly impacted by fires in southern hemisphere Africa and South America. To our knowledge, no other studies have used such a large dataset of in situ vertical profiles for deriving a climatology of the impact of biomass burning versus anthropogenic emissions on the strongest CO anomalies observed in the.
Source apportionment with the SOFT-IO tool In order to get information about the recent contributions of the different CO emission sources, we use the recently developed SOFT-IO v1. 0 tool  (Sauvage et al. , 2017b (Sauvage et al. , , 2017a ). Here we only give a brief overview of SOFT-IO; more details can be found in the reference paper of  Sauvage et al. (2017b). The SOFT-IO data are freely available in the IAGOS database (www. iagos. org) (https://doi. org/10. 25326/3, Sauvage et al. , 2017a). Along all aircraft trajectories, SOFT-IO couples FLEXPART retro-plume simulations over 20 days with anthropogenic and biomass burning CO emission inventories. At any given point in the IAGOS trajectories, it thus provides an estimate of the primary CO contribution (in ppbv) of the recent (20 days or less) worldwide emissions.
35 CO >0 represents the whole anomalies dataset). Note that all 250m-width layers are treated independently from each other. This means for instance that on a given profile, one single large pollution plume observed between 5 and 6 km of altitude will be treated as 4 (250m thick) anomalies. Some examples of vertical profiles at New York airport are given in Fig. 12. On the first profile on August 28 th 2004, anomalies between 4-6 km (below 3 km and above 6. 5 km) will be tagged as BB-like (AN-like). As previously explained (Sect. 2. 3), SOFT-IO does not simulate the CO background that represents in this example about 100 ppbv. The second example (Fig. 12 , right panel) is shown in order to illustrate the uncertainties affecting the transport of the plume (leading in this case to a 1-km error in the altitude of the plume).
SOFT-IO contributions The SOFT-IO tool was evaluated in  Sauvage et al. (2017b)  over the entire IAGOS dataset. Evaluation results have shown that SOFT-IO detects more than 95% of all observed CO plumes. The biases in the CO enhancements are usually lower than 10-15 ppbv in most regions, although the agreement is lower in the middle troposphere possibly due to numerous thin plumes of low intensity  (Sauvage et al. , 2017b). Note that, as previously explained in Sect. perspective, but also quantitatively) for interpreting the CO mixing ratios measured by IAGOS aircraft. In our study, we are not trying to quantify exactly the C AN and C BB contributions along all profiles. Instead, we are more interested in identifying the predominant type of emission sources (AN-like, BB-like or MIX-like) of all anomalies.
(2017b)  for the CO plumes of strongest intensity. At some airport clusters (Germany, ChinaSE), both the mean contribution and the strongest percentiles show a slight decrease in the highest anomalies. Reasons for this are not clearly identified. Due to a low number of points (below 100) in this range of extreme values, these distributions may not be as representative as for the anomalies of lower intensity. Nevertheless, these comparisons give us confidence on the ability of SOFT-IO to provide useful information regarding the CO anomalies observed in the IAGOS database, especially on the climatological point of view.
In order to make seasonal results comparable, as the number of flights varies depending on the season, all frequencies of occurrence are weighted by the total number of available data during each season (which explains why all bars in the CO >0 dataset are at 25%). The relative proportion of AN-like, MIX-like and BB-like anomalies at the seasonal scale is indicated on each bar. Considering all the CO anomalies no matter their intensity (i. e. the CO >0 anomalies set), results clearly indicate a dominant influence of the anthropogenic emissions whatever the season and the airport cluster. The only exception is Windhoek where the proportion of MIX-like and BB-like anomalies is large, in particular during fall. This is roughly consistent with the fact that most airports are located in a latitudinal band where AN emissions have a dominant contribution to the total emissions, except for Windhoek (Sect.
Vertical distribution of SOFT-IO contributions The climatological vertical distribution of the different anthropogenic and biomass burning contributions is shown in Fig. 23  for the entire dataset (CO >0 ). At the Germany cluster, EURO (TENA) contributions are the strongest below 6 km (10 km). The BONA contributions is the strongest in the lower free troposphere and decrease with altitude more quickly than the TENA contributions. The contributions from Asian source regions (CEAS, SEAS) reach their maximum higher in altitude, roughly between 6 and 12 km. In particular, the SEAS contribution peaks at about 10 km. At the clusters in North America, the TENA emissions mainly impact the lower altitudes while strongest contributions from CEAS and SEAS are found higher in the troposphere, between 4 and 12 km (with a maximum between 6 and 10 km).
7±1. 0% yr -1 ) and in Southern Hemisphere South America (-5. 1±3. 8% yr -1 ) maybe due to a reduced deforestation over the recent years. We provided an altitude-dependent distribution of CO mixing ratios based on the entire IAGOS dataset (about 125 million observations) in order to give the most general view of the CO levels typically encountered in the troposphere. Concerning the vertical distribution of the extreme CO mixing ratios registered by IAGOS over 2002-2017, the 99 th (99. 9 th ) percentile decreases with altitude from 750 (1619) ppbv below 1 km to 162 (229) ppbv above 12 km of altitude. The overview of all individual CO vertical profiles at the different airport clusters highlight frequent but irregular strong CO plumes in the free troposphere at most locations. In order to investigate the role of biomass burning relative to anthropogenic emissions, we simulated the recent primary CO contribution from both types of sources along all IAGOS trajectories with the recently developed SOFT-IO tool.
Atmos. Chem. Phys. Discuss. , https://doi. org/10. 5194/acp-2018-665 Manuscript under review for journal Atmos. Chem. Phys. Discussion started: 20 July 2018 c Author(s) 2018. CC BY 4. 0 License. Overview of the CO vertical profiles measured by IAGOS aircraft 4. 1 Distribution of the CO mixing ratios in the entire IAGOS database An overview of the distribution of all CO mixing ratios measured by IAGOS aircraft during the period 2002-2017 is shown in Figure 4 (see Fig. S4-7 in the Supplement for the seasonal distributions). Specifically in this section, the entire IAGOS dataset is taken into account (not only the tropospheric profiles available at the clusters of airports introduced in.
Acknowledgement The authors acknowledge the strong support of the  European Commission, Airbus , and the  Airlines (Lufthansa, Air-France ,  Austrian ,  Air Namibia ,  Cathay Pacific , Iberia and China Airlines so far) who carry the MOZAIC or IAGOS equipment and perform the maintenance since 1994. In its last 10 years of operation, MOZAIC has been funded by  Atmos. Chem. Phys. Discuss. , https://doi. org/10. 5194/acp-2018-665Manuscript under review for journal Atmos. Chem. Phys. Discussion started: 20 July 2018 c Author(s) 2018. CC BY 4. 0 License.
Data availability No new measurements were made for this review article. The IAGOS data are available on http://www. iagos. fr or directly via AERIS web site http://www. aeris-data. fr. The SOFT-IO products will be made available through the IAGOS central database (http://iagos. sedoo. fr/#L4Place) and are part of the ancillary products (https://doi. org/10. 25326/3, Sauvage et al. , 2017aSauvage et al. ,  , 2017b)).
4 Atmos. Chem. Phys. Discuss. , https://doi. org/10. 5194/acp-2018-665 Manuscript under review for journal Atmos. Chem. Phys. Discussion started: 20 July 2018 c Author(s) 2018. CC BY 4. 0 License. Overview of the CO vertical profiles measured by IAGOS aircraft 4. 1 Distribution of the CO mixing ratios in the entire IAGOS database An overview of the distribution of all CO mixing ratios measured by IAGOS aircraft during the period 2002-2017 is shown in Figure 4 (see Fig. S4-7 in the Supplement for the seasonal distributions). Specifically in this section, the entire IAGOS dataset is taken into account (not only the tropospheric profiles available at the clusters of airports introduced in.
and 2013 measurements, the MEGAN2. 1 (Model of Emissions of Gases and Aerosols from Nature version 2. 1) model was able to assess the observed ER variability only when its soil moisture activity factor γ SM was not operating and regardless of the drought intensity; in this case more than 80 % and 50 % of ER seasonal variability was assessed in the ND and AD respectively. We suggest that a specific formulation of γ SM be developed for the drought-adapted isoprene emitter, according to that obtained for Q. pubescens in this study (γ SM = 0. 192e 51. 93 SW with SW the soil water content). An isoprene algorithm (G14) was developed using an optimised artificial neural network (ANN) trained on our experimental dataset (ER + O 3 HP climatic and edaphic parameters cumulated over 0 to 21 days prior to the measurements).
Branch-scale ER assessment using an artificial neural network trained on field data The artificial neural network (ANN) developed in this study to assess branch-scale ER from Q. pubescens (henceforth referred to as G14) was based on a commercial version of the Netral NeuroOne software v. 6. 0 (http://www. inmodelia. com/, France; last access: 31 July 2018). The ANN was used as a multilayer perceptron (MLP) in order to calculate multiple non-linear regressions between a set of input regressors x i (the environmental variables measured at the O 3 HP) and the output data (the measured isoprene ER). The assessed ER (ER G14 ) was calculated as follows: ER G14 = w 0 + j =N j =1 w j,k × f w 0,j + i=n i=1 w i,j × x i , (3) where w 0 is the connecting weight between the bias and the output, N the number of neurons N j , f the transfer function, w 0,j the connecting weight between the bias and the neuron N j , w i the connecting weight between the input and the neuron N j , and x i the n input regressors.
The MLP optimisation of the weights w was achieved according to  Boissard et al. (2008). Every input regressor x i was centrally normalised. Two sub-datasets were considered, for the ND and AD plot respectively. For each sub-dataset, 80 % of our data were used for training and optimising the MLP, and the remaining 20 % were used for blind validation based on root mean square error (RMSE). Training-validation splitting was made using a Kullback-Leibler distance function available in NeuroOne v 6. 0. Only the non-linear hyperbolic tangent (tanh) function was tested as transfer function f. Up to N = 7 neurons (distributed in only one layer) were tested for every ANN setting. The overtraining phenomenon (a toolarge number of neurons vs. the number of input parameters) was checked against the RMSE training / RMSE validation evolution vs.
COOPERATE environmental database Ambient and edaphic parameters used for the ANN optimisation were obtained from the COOPERATE database (https://cooperate. obs-hp. fr/db, last access: 31 July 2018) and daily averaged for each day of our study. Ambient PAR (µmol m -2 s -1 ) measured above the canopy at 6. 5 m (LI-COR Li-190 ® ; Lincoln, NE, USA) in the ND plot was used as the PAR reaching all of the top canopy branches studied. Ambient air temperature (T , • C) measured at 6. 15 m (CS215, Campbell Scientific Ltd. , UK) in the ND and AD plot was used for both sets of branches. Since some precipitation (P , mm) values were missing (< 5 %) from the COOP-ERATE database during our data processing, P values from the nearby (< 10 km) Forcalquier meteorological station were used. The bias between cumulated P (P cum ) curves at both sites was assessed and considered in order to extrapolate the missing values at the O 3 HP site.
As P was cumulated over 7, 14, and 21 days, the resulting bias was negligible (≈ 1 %) and no further adjustment was made. Soil water content (SW, L L -1 ) and temperature (ST, • C) at -0. 1 m (Hydra Probe II, Stevens, Water Monitoring Systems Inc. , OR, USA) specific to each of the sampled trees were selected and extracted from the COOPERATE database; when soil data were missing, they were extrapolated from the nearest equivalent data point measurement. Daily mean PAR, T , P , SW, and ST were cumulated over a time period ranging from 1 to 21 days before the measurement. Corresponding values for the 2090-2100 period were used to assess the expected range of future climatic changes. They were derived from two ISI-MIP future projections forced along two representative concentration pathways (RCPs): the so-called "peakand-decline" greenhouse gas concentration scenario RCP2.
6 (optimistic or moderate scenario) and the "rising" greenhouse gas concentration scenario RCP8. 5 (extreme or severe scenario). All T and P data were extracted for the entire Mediterranean region from the global ISI-MIP dataset and subsequently averaged over the area. Using these present and future T , P , and PAR values (ISI-MIP derived), the corresponding present and future SW and ST were assessed by running the global land surface model ORCHIDEE (ORganising Carbon and Hydrology In Dynamic EcosystEms) over the European part of the Mediterranean region. The calculated SW and ST were averaged over this area. ORCHIDEE is a spatially explicit, processbased model that calculates the CO 2 , H 2 O, and heat fluxes between the land surface and the atmosphere. Vegetation species distributed at the Earth's surface are represented in ORCHIDEE through 13 plant functional types (PFTs).
For the record, θ w was 0. 15 m 3 m -3 at the O 3 HP. Assessed (ER MEGAN ) and observed (ER) isoprene emission rates were compared separately for ND and AD. However, given that the rainout shelter was implemented close to the commencement of our study in June 2012, measurements carried out in the AD plot were not distinguished, only in the case of this month, from the ones taken in the ND plot (AD and ND data were thus mixed for June 2012). For θ w < 0. 05 m 3 m -3 , and regardless of the θ w value, MEGAN2. 1 captured more than 80 % of the ER variability in the ND plot (y = 0. 15x 1,5 , R 2 = 0. 81, Fig. 3a ), but less (≈ 50 %) in the AD plot (R 2 = 0. 53 and 0. 54 for θ w = 0. 005 and 0. 01 m 3 m -3 respectively, Fig. 3b ). An overall overestimation of 25 % was associated with the MEGAN2.