dataesr/scientific-paragraphs-categorization huggingface dataset:
We conclude that this study indicates clearly that ClouDiV provides a reduced end-to-end delay and, an increased packet delivery ratio. Moreover, message dissemination with this proposal leads to a less congested network. We note that this Cloud-based message dissemination protocol can offer a number of desirable features such as the use of more computing capacities and various cloud computing services (i. e. processing, storage, bandwidth, platforms etc. ). Also, vehicles use less power than with a pure VANET, since data centers are widely used to forward data packets due to their larger coverage area and to their important bandwidth. As a future work, we will investigate the dissemination issue of a large amount of data such as video streaming for providing safety and non-safety services, in particular on very large scalable vehicular networks.
Thus, statistical confidence in the observations over that period is low, but it is possible that the errors in winds and sea surface temperature in the model are responsible for the discrepancy. If we exclude the effect of the observed winter minimum from our analysis, correlations in SIM2 are superior to SIM1, which demonstrates the improvement in the representation of aerosols in this part of the globe. We also compared the model AOD with the independent dataset from AERONET for 2007 (Fig. 4 ). AERONET data is very accurate and it is often used for the validation of satellite data  (Remer et al. , 2005; Kahn et al. , 2005; Schuster et al. , 2012). However, the horizontal representativity of AERONET data is much smaller compared to that of satellite data. The data is less adapted to make comparisons with the model than satellite data -it is localized in a single spot for each station compared to the 2 • × 2 • model data.
Back Close Full in the model correspond well with the SEVIRI field, except that the desert dust plume over Eastern Europe in the model is located more to the South. The AOD in SIM2 are much closer to the SEVIRI data than in SIM1. Low background AOD values in the model reveal a systematic underestimation over continents. This could be due to an absence of secondary aerosols. Table  4  shows how the MOCAGE simulations compare to data from the AeroCom model inter-comparison (http://aerocom. met. no/; Textor et al. , 2006 Textor et al. , , 2007)). AeroCom data is not based observations, but it is an independent dataset which indicates how MOCAGE relates to performances of other models. Values from SIM2 compare better to AeroCom ranges, by improving several parameters over SIM1. Emitted quantities fit better in SIM2, and there is an improvement in desert dust and sea salt lifetime as well.
show how different treatments influence the aerosol representation in the model and to which extent their uncertainties affect the model performance; and thirdly, validate the new parameterizations for emissions, wet deposition, and sedimentation in MOCAGE by comparing the model outputs with different satellite and ground observations. The article is organized as follows. In Sect. 2 we present the general description the model MOCAGE. The aerosol parameterizations in the model and their improvements are presented in details in Sect. 3. Section 4 describes all observational datasets used for comparison with the model. In Sect. 5 we define the model experiments and explain the method used to assess model performance. Results and discussions are presented in Sects. 6 and 7 where we compare MOCAGE results with different independent ob-.
Acknowledgements. This work is funded in France by  Centre National de Recherches Météorologiques (CNRM-GAME) of Météo-France  and  Centre National de la Recherche Scientifique (CNRS). The authors would like to thank the  AERONET PIs  and their staff for establishing and maintaining the sites used in this investigation. We acknowledge the MODIS mission team and scientists for the production of the data used in this study. We also acknowledge  NASA/Goddard Space Flight Center's Laboratory for Atmospheres  for developing and computing GPCP combined precipitation data and  NOAA/OAR/ESRL PSD, Boulder, USA  for providing it, Lamarque et al. (2010) for the emissions of carbonaceous aerosols and Global Fire Emission Database project for the fire emissions that we used. We thank  D. Carrer  and his collaborators for developing and providing their SEVIRI retrieved aerosol data.
show how different treatments influence the aerosol representation in the model and to which extent their uncertainties affect the model performance; and thirdly, validate the new parameterizations for emissions, wet deposition, and sedimentation in MOCAGE by comparing the model outputs with different satellite and ground observations. The article is organized as follows. In Sect. 2 we present the general description the model MOCAGE. The aerosol parameterizations in the model and their improvements are presented in details in Sect. 3. Section 4 describes all observational datasets used for comparison with the model. In Sect. 5 we define the model experiments and explain the method used to assess model performance. Results and discussions are presented in Sects. 6 and 7 where we compare MOCAGE results with different independent ob- servations, and evaluate a new set of parameterizations in MOCAGE to estimate their impact on aerosol burden, lifetime, concentration, deposition and optical depth.
Market policies have profound implications for consumers as well as for the management of resources. One of the major concerns in fish trading is species mislabelling: the commercial name used does not correspond to the product, most often because the product is in fact a cheaper or a more easily available species. Substitution rates depend heavily on species, some often being sold mislabelled while others rarely or never mislabelled. Rates also vary largely depending on countries. In this study, we analyse the first market-wide dataset collected for France, the largest sea food market in Europe, for fish species substitution. We sequenced and analysed 371 samples bearing 55 commercial species names, collected in fishmonger shops, supermarkets and restaurants; the largest dataset assembled to date in an European country.
, 2014; Rasmussen & Morrissey, 2008). Among the markers used, mitochondrial DNA sequences have emerged as near-universal markers for precise determination of species. The most frequently used sequences are partial cytochrome b, partial 16S or 12S ribosomal DNA, and partial cytochrome oxidase I (COI). The DNA sequence is then compared to reference sequences to identify the taxonomic group. The development of online databases containing thousands of DNA sequences has further enhanced the reliability and ease of use of these methods. It is particularly important that the reference sequence for the searched species is present in the reference dataset, so more complete datasets are more valuable  (Ekrem, Willassen & Stur, 2007). The Barcode of Life Database (BOLD,  Ratnasingham & Hebert, 2007)  currently contains almost 150,000 COI barcode sequences for almost 14,000 actinopterygian species.
Cytochrome b, the second largest, only has around 82,000 sequences listed in the GenBank nucleotide database. Additional features of the Barcode of Life project, such as linking sequences to vouchered specimens and specimen data, increase reliability compared to the notoriously high error rates in GenBank  (Harris, 2003; Rasmussen & Morrissey, 2008). With the success of the Barcode of Life Project for fish  (Ward, Hanner & Hebert, 2009) , its reference marker, COI, has been increasingly used for identifications and represents the majority of substitution studies in the last years. Other datasets for the regulatory identification of species substitutions have also been established for COI; for instance, in the Regulatory Fish Encyclopedia of the Food and Drug Administration  (Yancy et al. , 2008).
These products are particularly susceptible to be substituted as the customers and control agencies cannot easily recognize the species from the appearance of the product. Two collecting efforts have been started in parallel by the NGO Oceana, associated with the magazine Terra Eco, and the NGO Bloom, in collaboration with researchers from the French National Institute of Health and Medical Research (INSERM) and National Museum of Natural History (MNHN). These two initiatives took advantage of the development of citizen science to increase the sampling's coverage, both geographically and by place of purchase. They are both analysed and presented in this article, resulting in the largest European dataset for such a study.
FishLabel set The FL set was collected in pre-numbered tubes and stored in 95% ethanol until extraction. Each sample was divided at sampling in two tubes with the same sampling number. Data such as commercial name, Latin name when indicated, date, collector, location, brand name, shop or restaurant names were collected, as well as photographs of packages and samples when possible. The data were collected either on paper forms sent along with the samples or uploaded online using the smartphone application Epicollect  (Aanensen et al. , 2009). Only fillets or fish dishes were sampled as they are not readily identifiable and thus potentially easier to substitute. They were collected from fishmonger shops, restaurants, and supermarkets (either at the fishmonger department or industrially prepared, i.
: COIF268-5 ′ GAAACTGACTYATTCCTYTAATGAT3 ′ , COIF270-5 ′ AACTGACTTATTCCYYTAAT- GATYGG 3 ′ , COIR450-5 ′ GAAGTTAATTGCCCCAAGAATTGA 3 ′ , The TE samples were extracted, amplified and sequenced with the same primers by Spygen, a commercial company specialised in molecular identification (http://www. spygen. fr/). The sequences provided were analysed by the same person, and using the same approaches as the FL dataset. Sample descriptions and sequences are available in the Barcode of Life Database in the FSCF project (FCSF001-14 to FCSF291-14 for the FL dataset, FCSF292-14 to FCSF404-14 for the TE dataset), and in GenBank. Collector names, brands and precise collection data were anonymized. Photographs are included for samples when they do not threaten the anonymity of the data.
Molecular identification Three datasets were assembled according to the length of the sequences obtained (long, intermediate or short sized fragments). Within each dataset, pairwise distance trees were built with the taxon ID tree function included in BOLD to cluster identical sequences. These sequences were grouped in the alignment files, and sequence identities were also checked on the alignments. Each distinct sequence was then used to BLAST-search the Barcode of Life database. The long dataset was compared to the Species Level Barcode Records, while the medium and short datasets were matched to the Full Length Record Barcode Database to avoid issues due to insufficient overlap of the sequences with the reference dataset. Identification was determined by sequence similarity to the reference dataset  (Wong & Hanner, 2008) , and checked through their position  (Costa et al.
, 2012)  in the "Tree based identification" generated distance trees in BOLD. For species with low interspecific divergences (Gadus and Thunnus species), aligned sequences were compared to each other, to sequences from the BOLD, and to sequences from the FDA reference dataset for Seafood identification (http://www. fda. gov/Food/FoodScienceResearch/ DNASeafoodIdentification/ucm238880. htm). Additionally, we checked species-specific characteristic attributes and characteristic combinations on the alignments following  Lowenstein, Amato & Kolokotronis (2009).
Mislabelling determination For each sample, the list of admissible species that can be sold under the commercial name indicated on the menu, the price tag, or the box was determined by consulting a governmental website (http://www. economie. gouv. fr/dgccrf/Poissons, last checked on 25/02/2014). The sample was declared mislabelled if the species name determined through molecular identification was not in this list. We did not retain the commercial names obtained orally from the waiting staff in the calculations of the substitution rates. However, we have kept this information in the data files.
Grouping of commercial names The total number of commercial denominations retrieved from the completed forms was high (55 different commercial names), preventing statistical analysis of a large part of the dataset. The samples were thus grouped into broader commercial categories. For instance "cabillaud" (cod) was grouped with "cabillaud du pacifique" (pacific cod) and "morue" (a French nomenclature for dry and salted cod, whether Pacific or Atlantic) under "cabillaud. " This case and others like it reduced the number of categories to 30. We further decreased the number of categories by keeping only those for which at least 10 samples were available. All the other samples were grouped under the "other" category. However, after a preliminary analysis, it appeared that the mislabelling signal detected for the "tuna" category was mostly attributable to the samples sold as "bluefin tuna.
Sampling and sequencing We collected 291 samples using the FL protocol, out of which 276 could be sequenced. We obtained 172 long sequences, 97 intermediate length and seven short sequences. Fifteen samples (5,16%) could not be amplified at all, a failure rate comparable to other studies of this type  (Hanner et al. , 2011; Cawthorn, Steinman & Witthuhn, 2012). These included nine ready-made dishes, the single canned sample present in the sampling, two smoked fillets, and three restaurant dishes, all sources that are expected to show some DNA degradation. All 114 TE samples provided sequences (45 long sequences and 69 short). Both datasets together added up to 390 sequences. Nineteen of these were then removed because important information was missing, or doubts remained on the quality of the collected data.
, 2012). All these can therefore be considered as non-ambiguous, high reliability identifications. It was, however, less straightforward for three particular groups (European sea bass, tuna and rockfish), although the end result can be considered reliable. The first, "bar" (European sea bass, Dicentrarchus labrax), is represented by three divergent clusters in BOLD. Part of our samples are almost identical to samples from the UK and Spain; the rest are almost identical to samples from Turkey and Portugal. These two groups of sequences diverge by 2. 5%-3% from each other. Therefore, D. labrax samples had a grade C identification  (Costa et al. , 2012)  if considering only the sequences in BOLD. However, one of these groups of sequences is identical (or with one base divergence) with the highly reliable FDA208 Dicentrarchus labrax sequence in the Reference Standard Sequence Library for Seafood Identification of the FDA, and the reliability of the identification for the second group is also supported by reference sequences from independent datasets in BOLD.
, 2013)  or Ireland  (Miller & Mariani, 2010; Miller, Jessel & Mariani, 2012 , Table  3 ), and similar to that of the UK (7. 4% in  Miller, Jessel & Mariani, 2012). This low rate for France is very encouraging, but the origins of the differences between countries remain to be investigated. They might provide clues for a better resource and market management. While the substitution rate is low, there is a consistent pattern: a species is replaced by one with a lower commercial value. This pattern is also observed in other countries and hints at economic motives. We did not observe substitutions of species claimed to be sustainable by species that are not, whereas in the UK Pacific cod replaces Atlantic cod  (Miller, Jessel & Mariani, 2012). Our dataset contains very few samples of the Pacific species Gadus macrocephalus, although it is legally acceptable under the widely-used commercial name "cabillaud.
Moreover, for 16 samples collected in sushi restaurants the waiters replied upon enquiry that the tuna sold was bluefin tuna, which was never the case (data not shown but included in the BOLD repository). Although we excluded these samples from our analysis because the menu was not precise enough, this shows an absence of care or knowledge in the usage of this commercial name. The catches of this species were largely debated, and the presentation of this issue in the media was positive in influencing fisheries management  (Fromentin et al. , 2014). They probably make up the most lucrative fisheries in the world, driven by strong demand from the Japanese market (80% of the global catches)  (European Commission, 2009). This commercial importance led to severe overfishing during the 1990-2000s, with estimates of stock declines of 72% in the Eastern Atlantic, and of 82% in the Western Atlantic  (ICCAT, 2009).
In five cases out of ten, the people responsible for the last step before the fish reaches the consumer admitted intentional substitution for increased profit or consumer expectation reasons, in agreement with studies in other countries (as reviewed in  Jacquet & Pauly, 2008). There are no such last steps for the prepared meals and deep-frozen fillets. Our study was made possible by the involvement of dozens of volunteers throughout France. Citizen science has emerged in the last decades as a way for scientists to have access to large datasets extending the studies in space and time  (Hochachka et al. , 2012)  or to have humans performing tasks that computers cannot, as exemplified by the Galaxy Zoo  (Clery, 2011)  or FoldIt  (Cooper et al. , 2010)  projects. Some authors have distinguished different types of collaborative work between scientists and citizens, depending on the involvement of citizens in the research tasks  (Cooper et al.
, 2007). Our study had a mixed type of research management. It was initiated by non-scientists involved in controlling the economic use of natural marine resources. They were then joined by scientists to ensure that the study will meet the stringent criteria of peer-reviewed science, a model referred to as "participatory action research" by  Cooper et al. (2007). Finally, volunteers were recruited to enlarge the dataset, following a research model more common in citizen science. Our study has mobilized two types of citizens: the initiators of the study, who actively participated in all the research tasks; and the collectors, who enabled the large scale of the study by collecting samples. Involvement of non-specialists can represent a problem for the reliability of the sampling. For instance, if there were mistakes by inexperienced collectors, the most probable effect is additional "substitutions" recorded (false positives), with the result being an over-evaluation of the real number.
We detected no mislabelling in industrial products, while several restaurant owners or fishmongers acknowledged intentional substitution. This suggests that substitution is more important at the end of the supply chain and that control efforts must be directed at this level. Despite limitations in a few taxa, DNA barcoding based on the COI sequence provided fast, efficient and unambiguous identifications for most of our commercial fish samples even when only a short fragment was used, in line with previous studies  (Meusnier et al. , 2008). The BOLD hosted dataset gave a resolution superior to the one in GenBank, and the tools available with the database permit an easier evaluation of dataset quality and homogeneity. Although the substitutions appear infrequent compared to other studies and concentrated on some species and retail types, improvements can be made to increase the reliability of the market.
ACKNOWLEDGEMENTS We thank all our collectors for their great and thorough work, and  Baptiste Carton  and  Alexia Velasquez  for help with the lab work and the sequence management. Laboratory access and assistance was provided by the  "Service de Systematique Moleculaire " of the  Muséum National d'Histoire Naturelle  ( CNRS UMS2700  OMSI). We thank  Dusan Misevic  for commenting on earlier versions of this paper, and  S. Von der Heyden , an anonymous reviewer, and the editor  R. Toonen  for their constructive criticisms. We also thank  G. Lancelot  and  N. Schnell  for their English and content corrections.
latin name if indicated) Dataset length Similarity with BOLD sequences Molecular identification Origin Zipcode Tuna TE14 Thon rouge L 100. 00% Thunnus albacares Fishmonger filet 75 TE32 Thon rouge S 100. 00% Thunnus obesus Fishmonger filet 75 (Thunnus thynnus) TE109 Thon rouge L 100. 00% Thunnus albacares Fishmonger filet 75 FL0183 Thon rouge L 100. 00% Thunnus albacares Restaurant 50 FLID1031 Thon rouge L 100. 00% Thunnus albacares Fishmonger filet 75 FLID116 Thon albacore M 100. 00% Thunnus obesus Supermarket fresh filet 75 Cod TE63 Cabillaud S 100. 00% Melanogrammus aeglefinus Fishmonger filet 75 TE112 Cabillaud L 99. 69% Melanogrammus aeglefinus Fishmonger filet 75 (Gadus morhua) TE190 Cabillaud L 99. 85% Melanogrammus aeglefinus Fishmonger filet 75 FL0196 Cabillaud M 100. 00% Pollachius virens Restaurant 75 FL0572 Cabillaud M 100.
Many emerging applications such as collaborative editing, multi-player games, or fan-subbing require to form a team of experts to accomplish a task together. Existing research has investigated how to assign workers to such team-based tasks to ensure the best outcome assuming the skills of individual workers to be known. In this work, we investigate how to estimate individual worker's skill based on the outcome of the team-based tasks they have undertaken. We consider two popular skill aggregation functions and estimate the skill of the workers, where skill is either a deterministic value or a probability distribution. We propose efficient solutions for worker skill estimation using continuous and discrete optimization techniques. We present comprehensive experiments and validate the scalability and effectiveness of our proposed solutions using multiple real-world datasets.
, the team's skill, and that of the individual pdfs is minimized. For Sum-Skill-P (resp. , Max-Skill-P), the pdf that represents the team skill is a joint pdf computed by taking the sum convolution (resp. , max convolution)  [4]  of individual skill pdfs of the participants. The key challenge here is to be able to deconvolve the joint pdfs to estimate the individual pdfs accurately. Finally, we present a comprehensive evaluation of our solutions using two real-world datasets and demonstrate that they indeed estimate the true skills of individual workers effectively and compare with several appropriate baseline algorithms. Additionally, we demonstrate that our solutions are scalable using large-scale synthetic data. In summary, we make the following contributions: • Formalism: We formalize the problem of skill estimation for team-based tasks for different skill aggregations (Section 3).
• Solutions: We propose a comprehensive optimizationbased formulation considering both deterministic and probabilistic interpretations of skills. We propose principled solutions and present theoretical analyses (Sections 4 & 5). • Discussion & Experimental results: We conduct comprehensive experiments on multiple real-world datasets and a synthetic one that show that our algorithms are accurate and efficient (Section 7). We discuss the extensions of the problems in Section 6. u1 u2 u3 quality ( -→ Q ) t1 1 1 0 15 t2 0 1 1 10.
Naturally, the scores and blocks of a team are the sum of its individual worker's scores and blocks. Given available history of past games and their respective outcomes (scores and blocks), we intend to learn the skill of individual players in scores and in blocks. Maximum Skill Aggregation -Research paper coauthorship: For a team of researchers co-authoring a paper, the qualitative outcome of the work is often driven by the most skilled (or experienced) researcher. Similarly, the quality evaluation of a paper could be modeled simply as the number of citations it gets within a given time period. As a concrete scenario, the number of citations that a database paper gets is an indicator of its technical quality. In this example, we intend to learn each co-author's skill (expertise) in the database area.