dataesr/scientific-paragraphs-categorization huggingface dataset:
(2013). The use of information from multiple windows as a means of more accurately calibrating voltammetric data was pioneered by  Kogut and Voelker (2001) , who proposed the 'overload titration. ' To test their experimental design, we analyzed data from windows MW1-1 and MW1-5. Despite the very high [AL] T in window 5, the internal calibration slope remained low-biased, S max = 0. 85, due to the high [L 1 ] T in this sample. As a consequence, the [L 2 ] T derived from modeling window 1 is also low-biased and the RMS-RE of this fit (w1 + OV in Table  6 ) exceeds the experimental error of 3% by a factor of 4 or so. Interestingly, simultaneous calibration/direct modeling of the two curves together yields much improved fits to the data (w1 + w5 in Table  6 ). Finally, it should be noted that excellent results were attained using both variable and fixed α′ speciation models, with the same minimum RMS relative error of fit (0.
Overall, the top three most accurate sets of results were obtained using automatic unified analysis while the single most accurate set of results combined simultaneous calibration and parameter estimation. We therefore recommend that where sample volume and time permit, titration experiments be designed to include more than one detection window for all natural water samples, especially for coastal and estuarine waters. Note that even two windows can help substantially. It is vital that practical experimental designs for multi-window titrations be developed. 10. Everybody working in this field of research should verify their methods of data analysis, whether they use a simple spreadsheet model or custom software, by analyzing simulated data before applying it to data from real samples. We recommend using the simulated datasets presented here in Appendices 1 and 2.
Typology of approaches for modeling complexometric titration data. "Speciation models" are classified according to i) computational tools used (L, N, M), relationship between fitted Y/X and true dependent ([ΣML], [M x ] = [M meas ] or [M f ]) and independent ([M] T ) variables, and by inclusion of fixed or variable α′ (see below). "Calibration" describes the coupling of this step to speciation modeling. Early/first workers to apply an approach are indicated, along with maximum number of ligands modeled (1L, 2L, or nL) and whether overload (O), unified multiwindow (MW) analysis, or reverse (R) titration was performed. Approaches taken by participants for modeling the MW1 dataset are shown in {bold, bracketed} letters. [Available programs shown in square brackets].
Acknowledgments Logistical and partial financial support for Working Group 139, ' Organic Ligands -A Key Control on Trace Metal Biogeochemistry in the Ocean ', was provided by the  Scientific Committee on Oceanic Research (SCOR) , from grant  OCE-1243377  from the  U. S. National Science Foundation , and from national SCOR committees. The following funding agencies supported this work through grants or PhD scholarships. IP and DO were supported by the  Ministry of Science, Education and Sport of the Republic of Croatia , within the project ' Interactions of Trace Metals in Aquatic Environment '( 098-0982934-2720 ). SGS and MW were supported by the  New Zealand Ministry of Business, Innovation and Employment  ( CO1X1005 ) via a subcontract from the  National Institute of Water and Atmosphere Research , and a  University of Otago   Research Grant.
These structures were calibrated using the concept of Pareto optimality with three criteria pertaining to streamflow simulations and one to the seasonal dynamics of snow processes. The results were analyzed in the fourdimensional (4-D) space of performance measures using a fuzzy c-means clustering technique and a differential split sample test, leading to identify 14 equally acceptable model hypotheses. A filtering approach was then applied to these best-performing structures in order to minimize the overall uncertainty envelope while maximizing the number of enclosed observations. This led to retain eight model hypotheses as a representation of the minimum structural uncertainty that could be obtained with this modelling framework. Future work to better consider model predictive uncertainty should include a proper assessment of parameter equifinality and data errors, as well as the testing of new or refined hypotheses to allow for the use of additional auxiliary observations.
Additional criteria can then be introduced in relation to these auxiliary data or to specific aspects of the hydrograph (driven vs. non-driven components, rising limb, recession limbs, etc. ). In this perspective, multi-criteria evaluation techniques based on the concept of Pareto-optimality provide an interesting way to both reduce and quantify structural inadequacy  (Gupta et al. , 1998; Boyle et al. , 2000; Efstratiadis and Koutsoyiannis, 2010). A parameter set is said to be Pareto-optimal if it cannot be improved upon without degrad-ing at least one of the objective criteria. In general, meaningful information on the origin of model deficiencies can be derived from the mapping of Pareto-optimal solutions in the space of performance measures (often called the Pareto front) and used to discriminate between several rival structures  (Lee et al.
, 2014). Water abstractions for irrigation were estimated using information on historical water allocations provided by the Chilean authorities. Because these abstractions are likely to influence the hydrological behaviour of the catchment during recession and low-flow periods, they were added back to the gauged streamflow in Rivadavia before calibrating the models. In addition to streamflow data, remotely sensed data from the moderate resolution imaging spectroradiometer (MODIS) sensor were used to estimate the seasonal dynamics of snow accumulation and melt processes over a 9-year period  (2003) (2004) (2005) (2006) (2007) (2008) (2009) (2010) (2011). Daily snow cover products retrieved from NASA's Terra (MOD10A1) and Aqua (MYD10A1) satellites were combined into a single, composite 500 m resolution product to reduce the effect of swath gaps and cloud obscuration.
The second criterion (NSE log ) is related to the estimation of low flows and draws upon a modified, log version of the first criterion: Crit2 = 1 -NSE log = N d=1 log Q d obs -log Q d sim 2 N d=1 log(Q d obs ) -log Q obs 2 (3) The third criterion quantifies the mean annual volume error (VE M ) made in the estimation of the water balance of the catchment: Crit3 = VE M = N years y=1 V y obs -V y sim /V y obs N years (4) where V y obs and V y sim are the observed and simulated volumes for year y, and N years is the number of years of the simulation period. The fourth criterion (Crit4) differs between the two simulation periods. In calibration, snow-covered areas (SCA) estimated from the MODIS data were used to evaluate the consistency of snow-accounting modelling options in terms of snow presence or absence at the catchment scale.
To a large extent, this lack of performance can be attributed either to an insufficient coverage of the hypothesis and objective spaces or to uncertainties in the precipitation and streamflow data that were overlooked in this study. The choice of Pareto-optimality to characterize structural uncertainty can be criticized for leading to the rejection of many behavioural parameter sets (i. e. being close to, but not part of, the Pareto front) that might have been Pareto-optimal with different performance measures, calibration data or in-put errors (e. g. Freer et al. , 2003; Beven, 2006). Also, this concept should not be confused with that of equifinality. Both notions agree that it is not possible to identify a single, best solution to the calibration problem and that multiple parameters sets should be retained to give a proper account of model uncertainty.
Acknowledgements. The authors are very grateful to the  Centro de Estudios Avanzados en Zonas Áridas (CEAZA)  for its essential logistic support during the field missions and to  Gustavo Freixas  from the  Dirección General de Agua (Chile)  for providing the necessary streamflow data. The authors also thank  S. Lhermitte ,  D. López  and  S. MacDonell  for providing the MODIS data used in this study and  S. Gascoin  for informal advice and much useful discussion. Moreover, the authors thank the two anonymous reviewers for their interest in this work and for their useful comments that helped to improve the article. Edited by:  T. Kjeldsen.
We present a performance assessment of the European Integrated Carbon Observing System (ICOS) atmospheric network for constraining European biogenic CO 2 fluxes (hereafter net ecosystem exchange, NEE). The performance of the network is assessed in terms of uncertainty in the fluxes, using a state-of-the-art mesoscale variational atmospheric inversion system assimilating hourly averages of atmospheric data to solve for NEE at 6 h and 0. 5 • resolution. The performance of the ICOS atmospheric network is also assessed in terms of uncertainty reduction compared to typical uncertainties in the flux estimates from ecosystem models, which are used as prior information by the inversion. The uncertainty in inverted fluxes is computed for two typical periods representative of northern summer and winter conditions in July and in December 2007, respectively.
Acknowledgements. This study was co-funded by the  European Commission  under the  EU Seventh Research Framework Programme  (grant agreement no. 283080 , geocarbon project) and under the framework of the preparatory phase of ICOS. It was also co-funded by the industrial chair BridGES (supported by the  Université de Versailles Saint-Quentin-en-Yvelines , the  Commissariat à l'Energie Atomique et aux Energies Renouvelables , the  Centre National de la Recherche Scientifique ,  Thales Alenia Space and Veolia). We also would like to thank the partners of the ICOS infrastructure for providing a list of potential locations for future ICOS atmospheric sites. Edited by:  W. Lahoz.
We chose the mitochondrial COI gene because it is used as the barcode to catalog all living species on Earth  (Ratnasingham and Hebert, 2007) , and it has shown great potential in wildlife forensics with ready-to-use universal primers  (Dawnay et al. , 2007; Eaton et al. , 2010). Our analyses attempted to: (1) identify species and geographic origins of the seized pangolin scales; (2) estimate the total number of haplotypes in the confiscations to ensure that the confiscated scales were adequately characterized by our genetic analyses; (3) demonstrate the use of genetic information to assist in conservation and trade monitoring and (4) help to fill gaps in the global reference database for this mammalian group.
DNA extraction, amplification, and sequencing We used DNeasy Blood and Tissue Kits (Qiagen) and followed the handbook protocol for DNA extraction of the samples. DNA concentration was determined using Nano Drop8000 (Thermo-scientific, USA), and then diluted to the working concentration of 10-20 ng/µl. We amplified the Cytochrome c oxidase subunit I (COI) gene from each sample using two separate primer sets to ensure accuracy. The first applied M13-tailed mammal cocktails from  Ivanova et al. (2007). The second involved a new pair of primers (pangolin-COI-HZF3: 5 ′ -AGATTTACAGTCTAATGCTT -3 ′ and pangolin-COI-HZR1: 5 ′ -CCCATGTATCCAAAGGGCTCTT -3 ′ ) designed in Geneious Pro 6. 1. 6 software (Biomatters Ltd. ). This primer set amplified 793 bp of the 5 ′ end of the COI gene and span the standard barcode region.
The forward primer (pangolin-COI-HZF3) is located in tRNA(Tyr), which is a conserved region upstream of the COI gene. To compare with the reference data of M. crassicaudata (not represented by COI in Genbank), we amplified the first 402 bp of Cytochrome b (Cyt b) using the primer pair GVL14724-H15149 by  Gaubert et al. (2011)  in four scale samples (two samples from each of the COI haplotype H4 and H8). DNA was amplified in the following PCR mixture for the new COI primers: 5 µl 5× Colorless GoTaq Flexi Reaction Buffer (Promega), 0. 5 µl 10 mM dNTPs (Fermentas), 2. 5 µl 25 mM MgCl 2 , 1 µl 10 µM of each primer, 0. 2 µl 0. 02 U GoTaq Flexi DNA Polymerase (Promega), 1 µl DNA template, and ddH 2 O. The thermal cycling profiles for amplifications were as follows: denaturation at 94 °C for 1 min, and 35 cycles of 94 °C for 30 s, 52 °C for 20 s, and 72 °C for 50 s followed by one cycle of 72 °C for 2 min.
DNA data analysis The sample DNA sequences, together with the reference sequences of pangolins retrieved from GenBank (Appendix 4 and 5b in supplementary material) were assembled, edited and aligned in Geneious Pro 6. 1. 6 software (Biomatters Ltd. ). Sequence alignments were conducted with MUSCLE  (Edgar, 2004)  using default parameters, and subsequently checked by visual inspection. Final alignments have a sequence length of 600 bp in COI and 399 bp in Cyt b respectively. Outgroups were the domestic cat (Felis catus) for COI and the African tree pangolin (Phataginus tricuspis) for Cyt b. We identified relationships among the sampled pangolin sequences and pangolin sequences obtained from GenBank in two different ways. First, Bayesian phylogenetic analysis was performed in MrBayes version 3.
Species identification We obtained high quality COI sequences from 130 samples (out of 154 samples) in Confiscation 1 and 109 samples (out of 115 samples) in Confiscation 2. The weight for 130 samples in Confiscation 1 was 0. 52 kg and 0. 40 kg for 109 samples in Confiscation 2. A total of 13 haplotypes were identified in the 239 scale samples (Table  1 ). The Bayesian tree and the Maximum Likelihood (ML) tree for COI possessed identical topologies. The COI phylogeny clearly separated the African from the Asian species with high statistical support (Fig. 2(a) ). The clade of M. pentadactyla was well supported and five haplotypes were present in this clade. Four haplotypes were published   (Clement et al. , 2000). GenBank sequences of M. pentadactyla seized in mainland China, and our analysis identified an additional closely related haplotype in the Hong Kong specimens.
None of these five COI haplotypes was recovered from the seized scale samples. COI sequences from the confiscated scales and carcasses were divided into two distinct clades (Fig. 2(a) ). Clade A consisted of two scale haplotypes (H4 and H8) which did not cluster with any reference data. From scale samples of H4 and H8, two samples of each COI haplotype were selected for Cyt b sequencing and two Cyt b haplotypes were found corresponding to H4 and H8. Phylogenetic analysis of Cyt b gene also suggested H4 and H8 were in a distinct clade, which was a sister clade to M. javanica and did not belong to M. crassicaudata and M. pentadactyla (Appendix 5a in supplementary material). Clade B corresponded to M. javanica and it consisted of 23 COI haplotypes. There was a sub-clade (four haplotypes) in Clade B divergent from the other 19 haplotypes.
Estimating the total number of pangolin individuals and COI haplotypes The estimated number of pangolin individuals in Confiscation 1 and Confiscation 2 was 365 and 509 respectively based on the total weight of each confiscation and the average weight of scales of a M. javanica assumed to be 0. 61 kg. Hyperbolic function had the smallest SSD r value and was identified as the best-fit model to the data of Confiscation 1 and Confiscation 2. Using the asymptote of the estimated curve, the regression analyses suggested that the total number of COI haplotypes in Confiscation 1 and Confiscation 2 were 14. 86 and 11. 06 respectively (Table  3 ). When sequences from both confiscations were combined, the logarithmic function was identified as the best-fit model and yielded extrapolated estimates from 14.
Species identification Our COI phylogeny illustrates the large genetic differentiation between African and Asian pangolins, and further shows that the confiscated scale samples are not from Africa. This result is consistent with the inter-scale bristles found in the confiscated scale samples, an attribute that is unique to Asian pangolins  (Heath, 1992). Phylogenetic studies using mtDNA data  (Gaubert and Antunes, 2015; Hassanin et al. , 2015)  and comprehensive suites of morphological characters  (Gaudin and Wible, 1999; Gaudin et al. , 2009 ) also suggest the division of African and Asian pangolins. Our COI tree clearly showed the separation between M. pentadactyla and M. javanica, in line with other phylogenetic analyses using mitochondrial markers  (Gaubert and Antunes, 2015; Hassanin et al.
pentadactyla originating from Hong Kong were small, they carried a distinct COI haplotype. If additional analyses based on larger sample sizes corroborate this finding, then our data increases the potential for predicting the original geographic locations of illegal M. pentadactyla harvests. If each scale haplotype found in this study is collected from a particular geographic region, then the confiscated pangolins are highly likely to be harvested from wide geographical areas of Southeast Asia, and then gathered in central collection points for processing and shipping. In some cases, the illegal wildlife trade involves opportunistic, loosely connected networks of individuals  (Warchol, 2004). However, given the large size of the two Hong Kong seizures, it is possible that the operation of the harvest and smuggling involve an intricate network of hunters, traders and criminal syndicates.
However, it is becoming more difficult to find wild pangolins in Asia and although the transportation distance is much greater, it has now become commercially viable for traders to exploit African pangolins through recently established smuggling networks in Africa  (Challender and Hywood, 2012). Monitoring and regulating wildlife trade is essential  (Nijman, 2009). Our study demonstrates that DNA forensics is a powerful tool that can facilitate wildlife crime investigations  (Alacs et al. , 2009). Specifically, if regional authorities collect and maintain samples from confiscations for genetic investigations, then barcode genes such as Cyt b and COI may prove useful to investigations, especially given that there are validated primers and that much data now exists in GenBank available for comparison purposes  (Gaubert and Antunes, 2015).
Acknowledgments We thank the  Agriculture, Fisheries and Conservation Department of the HKSAR Government  for donating seized pangolin samples;  Kadoorie Farm  and  Botanic Garden  for providing facilities and support;  Yorkie Wong ,  Paul Crow , and  Alex Grioni  for assistance in handling carcasses and scale samples; two anonymous reviewers for comments and suggestions. MPM is a USGS employee and collaborator on this paper. Following the  USGS  scientific publication policy, any use of trade, product, or firm names is for descriptive purposes only and does not imply endorsement by the  US Government.
Posidonia oceanica meadows are declining at alarming rates due to climate change and human activities. Although P. oceanica is considered the most important and well-studied seagrass species of the Mediterranean Sea, to date there has been a limited effort to combine all the spatial information available and provide a complete distribution of meadows across the basin. The aim of this work is to provide a fine-scale assessment of (i) the current and historical known distribution of P. oceanica, (ii) the total area of meadows and (iii) the magnitude of regressive phenomena in the last decades. The outcomes showed the current spatial distribution of P. oceanica, covering a known area of 1,224,707 ha, and highlighted the lack of relevant data in part of the basin (21,471 linear km of coastline).
Previous studies have been based on a limited number of works with scattered quantitative data  23  or limited spatial extent  24  and presence/absence data at a very low spatial resolution  25. In addition, such datasets have never been made available online, with some limited exceptions  26. Furthermore, reliable historical information on the distribution of this habitat is largely lacking or has a low accuracy. Therefore, data on current distribution are scarcely informative of the trajectories of change and patterns of regression, which have been assessed only through a limited amount of information on meadow changes  4. This represents a strong limitation in providing a baseline of past ecosystem conditions  27. Thus, setting meaningful reference conditions, that might support regression monitoring and recovery assessment, remains a challenge.
Lengths of coastline with the known current and historical presence of Posidonia oceanica, the percentage of regression and the time range of data. Data referred to  1  Andalucia, Murcia, Valenciana regions, the Balearic Islands, 2 the continental coasts of France/Monaco,  3  Liguria, Tuscany, Latium, Apulia and Sardinia regions,  4  Polygons and point data: surface area referred to polygons only,  5  Point data only: no surface area is available,  6  Confirmed P. oceanica absence. coasts, meadows were not present except for a patchy distribution in the northern sector only. In the Eastern Adriatic Sea, small meadows covered 9 ha in Slovenia. P. oceanica beds were found along the northern Croatian coasts, with an area of 31,437 ha, whereas "presence points" only were available for the remaining Croatian and the entire Montenegrin coastlines.
A sharp border at 36°09'12" N, 33°26'39" E represented the eastern P. oceanica boundary along the continental coasts. Presence of P. oceanica was also reported along the Dardanelles Strait and in the Marmara Sea. Along the Moroccan coastline, P. oceanica was absent, except for the Chafarinas Islands (35°05′ N, 02°25′ E). According to limited information available, along the southern Mediterranean coast, the eastern boundary of P. oceanica meadows was located in Algeria, where they covered 4,072 ha, but the detailed distribution remains largely unknown. Furthermore, various maps and point data helped document the distribution of beds along the Tunisian coasts. P. oceanica was documented in the Gulfs of Gabès and Tunis, the Galite Archipelago, the Zembra Island and in other coastal areas, covering 518,685 ha.
Discussion As part of this study, we collated all the spatial information available in order to create a baseline distribution map of P. oceanica meadows across the Mediterranean basin. Where possible, we showed the current areal extent of P. oceanica meadows and potential trajectories of change. In the past, several attempts to collate such data across the Mediterranean Sea took place, but the present study represents a joint Mediterranean-wide effort, resulting in a unified and as complete as possible vision of meadows distribution and trends of change. The EUSeaMap project  34  produced a distribution map of marine habitats limited to the Western Mediterranean basin, including P. oceanica meadows, and used a broad geographical scale. The Barcelona Convention's Regional Activity Centre for Specially Protected Areas (RAC/SPA) collated existing data and maps without creating a complete Mediterranean-scale map (see Supplementary References UNEP-MAP RAC/SPA 2009, Supplementary Information online).
oceanica or the lack of data. Furthermore, all the cartography available on P. oceanica for the Mediterranean countries provided fragmented and approximate estimates of meadows distribution, or data at limited spatial resolution  25. Such scattered or low resolution information is too unreliable for planning and setting up future management regimes for the coastal zone. However, there are remarkable differences concerning the quantity of spatial data available in different parts of the basin. In particular, the western part has much more information available than the eastern part, where the "absence" of data is common, though it does not necessarily mean that P. oceanica is absent. Moreover, the MediSeH project can be considered the first effort to make an extensive collection of P. oceanica distribution data available online.