dataesr/scientific-paragraphs-categorization huggingface dataset:
We did not weight our observation in terms of scheme size, but treated each case equally. Hence, our analysis comes to put relatively more emphasis on a series of smaller-scale PES schemes that predominate in non-OECD countries. We built a database of in total 50 basic variables, a dozen of which we employ analytically. The main categories are:  6. evidence of environmental additionality (unit measured, method, level). Generally, we combined categorical (no hierarchy between levels), ordinal (hierarchical between levels), and continuous variables (cardinal ranking) in our analysis. S1 Database describes all variables in detail, but a few points are worth flagging. We registered which actors participated in different implementation stages, and classified them between public, private commercial (enterprises) and private non-commercial (NGOs, foundations, grassroots organizations) sectors (iii).
The data in the matrix have a strong internal consistency and reliability (Cronbach's Alpha = 0. 87 -S6 Table ). Fig  4  shows the first two dimensions of the analysis, representing a 52. 1% of the total variability of our 55 cases per 8 variables matrix. We additionally performed a cluster analysis (distance measure: Sorensen; group linkage method: group average) resulting in three distinct groupings of cases and attributes. The drawn border lines in Fig  4  delimit the PES schemes belonging to each of the three clusters. The combined CatPCA and cluster show which schemes have closest coordinates, and thus are more similar, the degree of concentration of PES schemes within groups, and the distance between groups. Variables with extreme values in one or both of the two axes define the cluster characteristics, since each axis is a vector composed by the weights of all the variables.
Discussion and Conclusion Meta-analyses can help us recognizing global empirical patterns of PES. As the PES literature flourishes and new case studies are continuously being added, we have the opportunity to gain more knowledge about commonalities in implementation and outcomes. In this article, we took advantage of the expansion of PES case studies in recent years to construct a global database. We used a fairly narrow and explicit definition of PES, in order to identify comparable schemes. The cases selected show a balanced number of schemes by geographical region, ecosystem type, ecosystem service and economic sector involved, showing that the systematic review procedure succeeded in limiting the risk of selection bias. For instance, the limited number of African PES schemes included in our study reflects the actual slower up-take of PES in this continent.
We included quantitative and qualitative data from what we see functionally as "genuine" PES schemes, using a narrow PES definition that ensures comparability. We also filtered out cases without the availability of sufficient reliable documentation. We then conducted some exploratory analysis of the emerging patterns of implementation, going from purely descriptive bivariate statistics to principal component analysis of emerging variable clusters, and finally to test some theoretically sustained hypotheses regarding the association of alleged key PES design variables with positive environmental additionality. From our descriptive analysis, we noted significant differences between publicly and privately financed PES schemes (e. g. public schemes feature larger in area and more costly schemes), but also between the different ES that these schemes target.
Acknowledgments The authors would like to thank the two anonymous reviewers for their helpful comments. Funding:  1. 1. 1. Agence Nationale de la Recherche   ANR 2010 STRA 008. 01  http://www. agence-nationale-recherche. fr/. 1. 2. European Commission  grant number:  DCI-ENV/2011/ 269520 , and the  CGIAR Forest, Trees, and Agroforestry Program. Funding: 2. 2. 1. The  ANR  funded part of the scientific meetings that contributed to data analysis and the writing of the manuscript. 2. 2. The grant number  DCI-ENV/2011/269520 , and the  CGIAR Forest, Trees, and Agroforestry Program  contributed to data analysis and the writing of the manuscript.
Dates: 2000-2014 German national Search in: Online 16 (15) 1 library https://portal. CatalogueSearch: All search dnb. de terms Open Greyhttp:// Search: All search terms 10 (10) 1 www. opengrey. eu/ HighWirehttp:// Search Title and Abstract 24 (20) 3 highwire. stanford. only: All search edu termsDatabases: HighWire- hostedDates: 2000 -Present British national Search: All search terms 250 (25) 1 libraryhttp://www. bldss. bl. uk Google Scholar Search: All search terms 500* (3) 3 Brazilian scientific Search: All search termsIn 2 (2) 0 electronic library field: All indexes onlinehttp://www. scielo. br/ 2. program design (monitoring, sanctioning, baselines, ES targeting, differentiation of payments); 3. program institutional arrangements (ES buyer and provider types, source of payments, pres- ence of intermediaries, transaction setting); 4.
MATERIAL Study Area Our study area comprises the Western Indian (20   1 ). The three oceans are considered separately in this study because they differ greatly among them with respect to climate, oceanographic characteristics, current dynamics and upwelling systems  (Tomczak and Godfrey, 2003). (Amandè et al. , 2010). By-catch data from the tropical tuna purse seine fisheries in the Eastern Pacific Ocean from 1993 to 2011 was collected by the Inter-American Tropical Tuna Commission (IATTC) observer program, with 100% coverage of the purse seine vessels of carrying capacity greater than 363 metric tons. Those observer programs record all the captures in each set, in numbers when possible and in weights otherwise. The objective of those programs is to estimate the amount of by-catch species in order to increase their knowledge which will allow developing measures to reduce their incidental mortality.
Environmental Variables Environmental data were extracted from the AquaMaps database  (Kaschner et al. , 2008)  at 0. 5 • resolution and stored as sets of cell attributes in a Half-degree Cell Authority File (HCAF) along with their associated Land Ocean Interactions in the Coastal Zone (LOICZ) (http://www. loicz. org) and C-squares ID numbers (http://www. marine. csiro. au/csquares). The HCAF contains such environmental attributes for a grid of 164, 520 halfdegree cells over oceanic waters. We considered 4 environmental variables as potential predictors of C. falciformis and C. maculata habitat distribution: depth, sea surface temperature (SST), salinity, and primary production (Prim. Prod). These environmental variables were selected by their general relevance for (epi) pelagic species and their relation to the specific oceanographic conditions in each Ocean  (Sund et al.
This step is necessary to find any collinearity between explanatory variables  (Louzao et al. , 2012). In addition, we evaluated percentage of contribution of the environmental variables to the MaxEnt model based on a jackknife procedure, which provides the explanatory power of each variable when used in isolation. Suitability maps for C. falciformis and C. maculata were constructed using the MaxEnt algorithm with "dismo" package in R software  (Hijmans et al. , 2013).
Pseudo-Absence Data Generation The occurrences for silky shark and rough triggerfish were obtained from the same dataset in each Ocean. All the sampled occurrences were selected in the Indian Ocean and Atlantic Ocean dataset. In contrast, in the Pacific Ocean 1000 subsamples were randomly selected to compare similar number of occurrences between oceans. The total fishing effort is showed for each Ocean in Supplementary Material Figure  3. The absence of species in a set may be explained by three reasons: (1) the species was not present, (2) the species was present but escaped from the net and it was not captured or recorded, (3) the species was captured but it was not recorded by the observer. The species absence in a specific set could be reconstructed from the general species list but introduces a risk of creating erroneous data.
In this work, shark and triggerfish data was considered presence-only, as true absences were unknown. Where absence data are unavailable to use in habitat models, an alternative approach is to generate pseudo-absences that should, ideally, also account for any spatial bias in the sampling effort  (Phillips et al. , 2009). For that reason, we have generated pseudo-absences for model evaluation purposes. We generated  See Table  1  for the explanation of the variables and data sources, and the maps in the Supplementary Material (Figure  2 ) for the spatial distribution of the variables. the pseudo-absences following the next method: pseudo-absence points were selected randomly from across the sampled area in each ocean. Furthermore, an equal number of pseudo-absence points as presences points were used for the random selection method  (Senay et al.
Model Evaluation The accuracy of the model and the five replicate model cross-validations were evaluated using the area under the receiver operating characteristic curve (AUC)  (Fielding and Bell, 1997). Given the defined threshold value, a confusion matrix or error matrix  (Pearson, 2007) , which represents a cross-tabulation of the modeled occurrence (presence/pseudoabsence) against the observations dataset, was also calculated based on the following indexes  (Pearson, 2007) : sensitivity (proportion of observed occurrences correctly predicted), specificity (proportion of pseudo-absences correctly predicted), accuracy (proportion of the presence and pseudo-absence records correctly assigned), and omission error (proportion of observed occurrences incorrectly predicted). The modeled probability of species presence was converted to either presence or absence using probability thresholds obtained using two criteria: sensitivity is equal to specificity, and maximization of sensitivity plus specificity, following Jiménez-Valverde and Lobo (2007).
Projections for the Twenty-First Century Habitat suitability of C. falciformis and C. maculata was modeled at present  (2001-2010/11 ) and future (2090-2099/2100) conditions under the A2 climate change scenario  (Muthoni, 2010). The A2 scenario (concentrations of carbon dioxide of 856 ppm by 2100)  (Muthoni, 2010; Rombouts et al. , 2012) , which was used in this study describes a very heterogeneous world with high population growth, slow economic development primarily regionally oriented and slow technological change. The same environmental variables used for the present conditions were also obtained from the Aquamaps database for the future climate under the A2 scenario  (Kaschner et al. , 2008). Once the habitat models were built on the basis of present environmental data and occurrence observations, they were projected to future climate conditions to assess the habitat distribution response to climate change.
(2012)  did. Moderately high AUC and overall prediction accuracy around 0. 70 were found for both by-catch species in different oceans. Our distribution models were able to predict habitat suitability for silky shark and rough triggerfish over a more extensive area than that covered only by the observer data (ocurrences). The observer dataset we used contained only silky shark and rough triggerfish presences. We addressed this drawback by randomly generating pseudo-absences  (Senay et al. , 2013)  and running five times the prediction to account for the robustness of the models. However, the correct selection of pseudo-absence data directly affects the accuracy of model prediction. For that reason, the accurate identification of the area (in this case, the sampled area and not areas out of the sampled area) for the creation of pseudo-absences was essential for the correct model performance.
The differences were based on the different sources of information used (museum collections, different databases, literature references) compared to our work which contains a large number of offshore observations since it is based on observer programs covering the wide distribution of the tropical tuna fisheries. In that sense, the presence data of our sampling provides new information about the distribution of the two species. This new information may be a result of the expansion of the FAD fisheries. The habitat models derived in this study suggest that C. falciformis and C. maculata responded mainly to variation in SST in the three oceans. These by-catch species are often distributed in warm waters and aggregated around floating objects (e. g. , logs, Fish Aggregating Devices) in productive areas  (Dagorn et al.
There is not best model, and the choice should be driven by the question and the objective of the study. In this work, the MaxEnt habitat modeling method allowed in an easy way to obtain essential information with few environmental variables about pelagic species. However, the gained experience leads us to discuss several aspects which must be considered and improved applying future habitat models. The selection of the occurrence by-catch data from the fishery not targeting those species can lead to assume that the data quality is not enough. However, we demonstrated that observer data is been used in multiple ecological and habitat studies similar to the one described here. Nevertheless, further increase of the coverage rates (in the case of the Atlantic and Indian Ocean) and the sample size is essential for doing comparisons between years and periods.
ACKNOWLEDGMENTS The occurrence data analyzed in this study were collected by  AZTI ,  Institut de Recherche pour le Développement (IRD) ,  Instituto Español de Oceanografía (IEO) , and  IATTC  ( Inter American Tropical Tuna Commission)  through the  EUfunded Data Collection Framework (DCF, Reg (EC)   1543/2000, 1639/2001 , and  665/2008 ) and  IATTC observer program. We wish to acknowledge to  Nick Vogel  ( IATTC ) for the process and management of the by-catch data and especially to  Maite Louzao  for her help and advices with the analyses. This study was part of the Ph. D. thesis conducted by the first author (NLO) at AZTI marine institute and funded by  Iñaki Goenaga  ( FCT grant ). The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
An example of phase diagram Let us illustrate our approach with the phase plane description for some value of the parameters, for instance, Fr = 0. 3 < 1 and Bo = 0. 8 > 1/3. The corresponding curve C 0. 3,0. 8 is shown in the left panel of Figure  1. Since the values 0. 3 and 0. 8 are exact rational numbers, it can be plotted simply relying on a discretisation, e. g. with Matlab or drawn with a certified topology technique using specialised programs such as the algebraic curves package of Maple or Axel (see http://axel. inria. fr). In particular, this means that loops of whatever size are preliminary detected relying on exact (given certain precision) numerical and algebraic computations. Note that the curve is symmetric with respect to the h-axis. This geometric property is general in our model, since the equations involve only the square of the derivative h ′.
2005; White et al. 2012). The SimCalc project has spent more than 15 years investigating the impact of combining representational affordances of the SimCalc software and connectivity affordances  (Hegedus and Roschelle 2012). Classroom connectivity reformats the interaction patterns between students, teachers and technology. This work stresses the importance of the student experience being mathematical. As students participate in mathematical ways, ownership of their constructions can become personal and deeply affective, triggering various forms of interaction after their work is shared and projected into a public display space. As an example, we pull from an activity in an Algebra 2 course in high school in which students are investigating mathematics of change for second-degree functions, their representations, and the role of the parameters "a", "b", and "c" in y = ax 2 + bx + c.
From Outside to Inside the Classroom: Augmented Reality There is work also being done to bridge the gap between technology used outside of an educational setting and the technology used inside an educational setting. The work of Chris Dede and team on EcoMuve and EcoMobile aim to capitalize on mobile devices as a way to collect data in the world for analysis and discussion in the classroom, or some other educational setting. One could argue this work aims to balance the pragmatic and the epistemic value of technologies linking an individual or groups of individuals with the world around them enabling them to mathematize their world, and integrate their actions as a connected individual with their actions as a classroom student. We draw on the example of EcoMOBILE (Ecosystems Mobile Outdoor Blended Immersive Learning Environment), which provides a learning experience in which students access virtual information and simulated experiences (Augmented Reality-AR) while immersed in real world ecosystems in students' own area  (Kamarainen et al.
2015). This science education project was designed to complement the EcoMUVE curriculum, which is based on Multi-User Virtual Environments (MUVEs) experienced on computers in the classroom. Within the EcoMUVE environment, students investigate ecosystems and collect data in immersive simulated ecosystems. This environment is designed to help students learn the system dynamics and complexity within ecosystems and causal relationships from introducing or changing something to, or within, the system. In their work, students collected data using smartphones from a local field trip to nearby pond and brought their data and field notes back to the classroom for further investigation in the virtual pond within the EcoMUVE system. In their work in next-generation virtual reality interfaces for mathematics and geometry education  Kaufmann and Schmalstieg (2003)  developed a three-dimensional geometric construction tool called Construct 3D.
This paper presents the Flex5Gware project, whose goal is to deliver highly reconfigurable hardware (HW) platforms and HW-agnostic software (SW) platforms for 5G network elements and terminal devices. Flex5Gware will enable 5G HW/SW platforms to meet the requirements imposed by the growth in mobile traffic and the diversity of applications by increasing their capacity, reducing their energy footprint, and enhancing their scalability and modularity. To put in place this vision in this early stage of 5G PPP research, Flex5Gware is designing and prototyping key building blocks of 5G HW/SW platforms. In particular, the developed technologies will be evaluated and demonstrated with proofs-of-concept by the end of the project.
g. , data rate, capacity, latency, resilience). Energy efficiency. Scalability, modularity, and reconfigurability, especially when multiple Radio Access Technologies (RAT) are considered. To achieve these requirements, highly reconfigurable HW platforms and HW-agnostic SW technologies (targeting both network elements and devices) are needed to enable a smooth transition from 4G mobile wireless systems to 5G. In particular, Flex5Gware is investigating a number of technical aspects related to HW platforms: analogue HW components for 5G multi-RAT wireless systems, advanced semiconductor technologies and new HW architectures, mixed-signal technologies and conversion stages, digital front-end architectures and flexible HW/SW function partitioning. Similarly, Flex5Gware is also investigating technical aspects related to SW platforms: modules, functions and interfaces enabled by virtualization technologies, towards a high performance, energy efficient, flexible and reconfigurable, low cost operation and effective service deployment tailored to users' preferences.
A. Approach The development of the Flex5Gware vision described in the previous section, entails many component and system design challenges that will be addressed through novel technologies. Each technology proposed in Flex5Gware is contributing, at least, to one of the following goals (see Section IV for more details): 1. To improve the energy and spectrum efficiency (the latter includes the increase of the operated radio frequency bandwidth via mmWave-band usage and aggregation of spectrum at frequencies below 6 GHz). 2. To improve the modularity and flexibility (which implies increased reconfigurability and scalability). The Flex5Gware consortium has identified four groups of technologies that are necessary to reach these goals: RF frontends and antennas, mixed-signal technologies, digital frontends and HW/SW function split, and SW modules and functions.
Moreover, HW abstractions also enable to simplify the definition of protocol and control policies, thus decoupling them from specific technologies or manufacturers. More specifically, the project is defining interface abstractions enhanced by energy awareness offered to the upper layers (e. g. , control and management), as well as a generic architecture supporting emerging 5G PHY capabilities (e. g. , multiple antennas, dynamic bandwidth, sensing/monitoring operations) and parallel execution of MAC protocols. Flex5Gware is also designing and implementing control and management tools including context extraction and awareness, in order to enable real-time technology-agnostic deployment, composition, and reconfiguration of network protocol stacks and SW modules available across multiple nodes thanks to sub-ms level synchronization schemes.
C. Increasing the overall achievable capacity provided by 5G communication platforms As pointed out in the introduction, the anticipated 1000 fold increase in mobile data traffic over the next decade, the massive amount of new applications that networks will have to support, from internet of things (IoT) to Ultra High Definition-TV (UHDTV), as well as the effective service development and deployment tailored to users' preferences, impose the increase of the overall achievable capacity provided by 5G communication platforms. In the analogue domain, Flex5Gware is providing novel HW solutions designed to support the additional bands in the mmWave range, which are expected to enable the provision of higher mobile data volume per geographical area and higher typical user data rates. In terms of mixed-signal technologies, the following Flex5Gware contributions will increase the achievable capacity of 5G networks: full duplex operation, and direct RF signal generation.
D. Decreasing the overall energy consumed by 5G communication platforms Communication systems such as Radio Access Networks (RAN) are consuming a significant share of the overall energy consumption  [16]. The situation is going to worsen because of the anticipated mobile data traffic increase. The impact will be high in terms of both the environment (due to CO 2 emissions) and operational expenditures (OPEX) as reflected in the cost per delivered bit. To that respect, there is a strong need to increase energy efficiency of communication networks. In Flex5Gware, this is being addressed by the design of different network components and including deployment strategies. For example, in terms of mixed-signal technologies, Flex5Gware is implementing Peak to Average Power Ratio (PAPR) reduction, predistortion and envelope tracking techniques that can improve the energy efficiency of a base station PA by up to 50 % (and up to 30 % at the device level).