dataesr/scientific-paragraphs-categorization huggingface dataset:
Catches The Sea Around Us project uses a geographic information system to map global fisheries catches from 1950 to the present, with explicit consideration of coral reefs, seamounts, estuaries and other critical habitats of fish, marine invertebrates, marine mammals and other components of marine biodiversity  (Watson et al. , 2004 ; http://www. seaaroundus. org). In the present study, we linked directly to the underlying spatial catch dataset and we used these catches as 'observed catches' to evalu-ate model runs. The catches for the base year, 1950, were also used to parameterize the landings by fleets in the initial underlying Ecopath initial model.
Stock reduction analysis We extracted catches by functional group and by year from the catch database described above. Based on this, we constructed a time series in Ecosim with 'forced catches' , i. e. a SRA  (Kimura et al. , 1984) , implemented as a stochastic SRA  (Walters et al. , 2006)  with two, additive, search criteria. The first was that catches could be replicated in Ecosim, which notably requires that the populations are maintained through the simulation. SRAs are very sensitive to the initial biomass, B 0; if the initial biomass is too low the population will crash. The second criterion was to avoid B0 that are too high as this results in biomasses being unrealistically steady. For this, we assumed as a target that the terminal fishing mortality for fish in the world ocean should be close to the natural mortality for the species groups.
Model implementation framework We developed a framework for constructing the modelling complex leading to the global ocean model, EcoOcean, through database extraction following  Christensen et al. (2009). The framework extracts spatial models for different areas and with different spatial resolutions. By default the area is global (or rather 90°N to 80°S ignoring the Antarctic landmass), and the resolution is 0. 5°latitude by 0. 5°longitude. The first step in the process was to develop an Ecopath model  (Christensen & Pauly, 1992)  to be used for the modelling framework. For this we used a database-derived model  (Christensen et al. , 2009)  updated with spatial and ecological information from global databases (as explained above). The model biomasses were updated for those groups where information was available from databases (see  Christensen et al.
, 2009). Then, catches were read from the catch database for the base year (which here was 1950). Using a diet database from FishBase  (Froese & Pauly, 2006) , the diet for each functional group was averaged where there was species-specific information available. These diets were then used to replace the initial diets in the Ecopath model. Then the framework read the fleet effort database  (Anticamara et al. , 2011) , which was by country and gear type (see Table  2 ), and performed the modifications and interpolations that were described in the 'Fleet effort distribution' section above. It then constructed time series with effort by year and by gear (Fig. S1  in Appendix S3), and these time series were stored in the model database. Information about preferred depth zones by species were read by species and averaged by functional group to obtain average depth distributions for the different functional groups in the model.
Programming environments The modelling framework was implemented as a VB. NET module that was coupled with the EwE model. Through its integration with EwE the model had full access to the EwE graphic user interface, which facilitated both model development and evaluation. The model runtime for a 56-year run (with a 10-year spin-up period) with 1°resolution is 90 min on a 24-core PC, and more than 12 h for the version with 0. 5°resolution. While this is fast for a global spatial model it must be noted that evaluating uncertainty calls for a very large number of model runs, and this can be prohibitively time-consuming. With this in mind, the model was ported to a LINUX cluster computing environment (at Compute Canada's WestGrid). Given the amount of. NET legacy code involved, which is tied to the Windows environment, a version of the spatial model without the user interface was prepared so that it could be run under Mono 3.
As a consequence we note that in this study not applying a formal fitting procedure minimizes the chance that we will be overfitting the model, which tends to result in poor predictive capability. We compared observed catch data by functional groups aggregated spatially with the catch data predicted by the global spatial model. Even though the model used the average catch by fishing fleet in 1950 as its baseline -and this does come from the observed catch data -the predicted catches over time are based on the fleet effort database, which is independent of the catch database and is spatially explicit. We therefore concluded that the observed and predicted catches over time can be considered independent. Figure  1  serves to illustrate this conclusion. The immediate conclusion when comparing observed versus predicted catches by functional group for the years 1950 to 2000 (Fig.
Closer examination of Fig. 4  shows that some key problems are overestimation of the small pelagic catches and underestimation of the medium-sized pelagics. Overall, however, this almost evens out. The figure also reflects that the model does not capture well the recent stagnation in global catches -assuming it is real; 'observed' catches are estimates as well. The key factor for this is the continued increase in small pelagics and small demersals, both of which will have to be examined in more detail. However, we expect that updates of the global effort database will help remedy this. With this study we have developed a functional global spatialtemporal ocean model that can be used for evaluating how major drivers in the form of environmental productivity combined with fishing effort affect the ocean.
, 2010). It is a shortcoming of the effort data included here that we do not have effort from outside the LMEs for non-tuna fleets. This is a topic that will need consideration in coming iterations of the model. There are still considerable improvements needed for the modelling complex presented here before it is fully functional for all its intended uses. On the parameter front, we do not, for instance, include temperature effects directly in the food web model, only indirectly through the linked climate model. For future development we will extend the modelling complex with a focus on: (1) governance aspects to evaluate fisheries policies that promotes resilience to climate change; (2) the interplay between aquaculture and capture fisheries; and (3) global seafood trade. However, this study shows that global modelling efforts are not only needed but are indeed both possible and making progress.
ACKNOWLEDGEMENTS The initial version of this modelling complex was developed in cooperation with the  Netherlands Environmental Assessment Agency (PBL) , and we notably thank  Rob Alkemade  for ongoing and inspiring cooperation. The development was supported by the Nereus -Predicting the Future Ocean activity. M. C. was supported by the  EC Marie Curie CIG  grant to BIOWEB and the  Spanish Research Program Ramon y Cajal. R. W. was supported by an  Australian Research. Council Discovery  grant. V. C. and W. W. L. C. acknowledge support from the  Natural Sciences and Engineering Research Council of Canada , and W. W. L. C. also from the  National Geographic Society. We thank  Compute Canada  and  WestGrid  for support that made it possible to run the modelling complex on Linux cluster computers.
To allow the modelling, topographic survey and the inside and outside scans via terrestrial laser-scanner (TLS) has been realized and used. A first method described in  (Koehl, 2014)  was performed in a CAD environment (Bentley Microstation) by using the possibilities of partial display of the point clouds and by using especially the familiar tools of CAD engine. This first method required long manual work, but allows a detailed approach by using the principles and the methods known for modelling in a CAD environment. This project had to define if the use of a software that proposes procedural modelling, in particular the ESRI's CityEngine 2014. 0 package would gain efficiency in this context of modelling of existing object, then to see if the same environment could be used to realize easily simulations within the framework of archaeological restorations applied to the today disappeared chapel.
Modelling environment CityEngine is a three-dimensional modelling software application developed now by Esri. It is specialized in the generation of 3D urban environments with the procedural modelling approach. City Engine enables the efficient creation of detailed large-scale 3D city models with merely a few clicks of the mouse instead of the time-exhaustive and work intensive method of object creation and manual placement. The acquisition of CityEngine is aiming to push the innovations in 3D GIS and geodesign technology  (Sostaric, 2012). In fact, the realism of computer graphics images requires the creation of objects (or scenes) of increasing complexity, which leads to very huge costs. Procedural modelling can help to automate the creation process, to simplify the modification process or to generate multiple variations of an object instance.
CityEngine is experienced for mass modelling  (Mueller et al. , 2012)  with more or less random generations. But the examples and applications in the field of modelling of very detailed scenes with modelling patterned after the reality is still rare and limited. That is why, it could be interesting to use this software application in order to represent places. Experts and laymen study historical structures closely. Digital models of these elements of our cultural heritage are valuable tools for analysis, reconstruction, and virtual display. While the focus of cultural heritage digitization is still on 3D modelling of important major monuments, there are often additional demand for modelling larger settlements. Such settlements might be interesting or might only form the context for a monument.
We had a photogrammetric survey of the interior and exterior of the church. We finally also used photographs to create realistic textures for the model. 2. 2 CityEngine as modelling environment  (Mueller, 2007 ) defined a number of ground breaking techniques for the procedural modelling of 3D architectural contents which make up the foundation of CityEngine today. CityEngine has been presented for the first time outside of the research community in 2009 as a commercial product (Procedural inc. ). Then it was acquired in 2011 by Esri, one of the most powerful and well-known GIS Company. The main usefulness and highlight of CityEngine is to supply 3D contents from GIS data and also generate scenes and 3D models. Once the database loaded in CityEngine such as DTM, roads axes, footprints of buildings, vegetation, etc.
, the application of procedural rules can help to build the 3D entities. To better understand the process of generating 3D models, construction rules of 3D entities have been analysed and assigned in order to model a historic castle, the Engelbourg, Thann, France  (Koehl et al. , 2014). (Piccoli, 2013)  presented also a very complete overview of the potential use of CityEngine in archaeological projects. CityEngine is a 'Procedural Modelling Application'. This means that CityEngine uses rules to generate the 3D Model according to the process described in Figure  5. Beginning from the base form, we have to describe construction rules translated in CGA language allowing to modulate the forms (extrusion, division, repetition, etc. ). To write a rule, the user can use a rule editor that works in a direct visual mode or means code writing.
Edition of reconstruction rules applied to facades There exist also other software applications allowing to edit rules, particularly in the case of facades for which we can use a "wizard"  (Mueller et al. , 2007). The "facade Wizard" is an adapted tool allowing the user to create complex CGA rule models for facades. One of the advantages is that no CGA codes is needed to be written by the user, it is directly generated by CityEngine as background task. So complex architectural structures can be generated in an effective and fast way. This "wizard" uses as first entry an image of the facade. The image can directly be ortho-rectified by using a simple algorithm based on 4 coplanar points. As a result, the ortho-image in. PNG format can then be segmented in a following stage. At this stage, the facade is going to be divided horizontally into floors or levels, then every level is going to be vertically divided into columns, doors or windows, etc.
-Assets insertion: we insert then assets after new subdivisions and the preparation of the surfaces. The main front facade will be used as illustration of this asset insertion and as its resizing fitted to the geometry of the building (Figures 10). As we can see, the detailed elements cannot be directly modelled without difficulties with CityEngine. Then we use then assets created by means of other modelers: we summarize below the various methods of asset generation. From a point cloud, we can generate meshes by using the mesh software associated to scanners. However, the import of these meshes is not adapted to CityEngine who represents all the edges in a visible way. Thus an excessive large number of meshes does not allow an interesting description.
DISCUSSION AND CONCLUSION Through this project we have tried to handle the problem of detailed modelling by testing the possibilities of use of CityEngine and procedural modelling. We can tell at first rather long handling of CityEngine, because it is necessary to master the various tools. But this handling is also necessary within the framework of other modelers. The CGA language is well documented and numerous examples are accessible (CityEngine Blog, 2015; CityEngine Tutorial 2015). However it is not very obvious to manage especially that it is very specific to this environment. The unique use of CityEngine does not allow to reach models with sufficient detailed resolutions. Indeed, it seems efficient to work in parallel with other modelers to be able to develop objects or parts of detailed objects as assets.
Wide Web (WWW) alone has about 50 × 10 9 indexed web pages, so that their classification and information retrieval processing becomes a formidable task. Various search engines have been developed by private companies such as Google, Yahoo!, and others which are extensively used by Internet users. In addition, social networks  (Facebook, LiveJournal, Twitter, etc. )  have gained much popularity in the last few years. In addition, the use of social networks has spread beyond their initial purpose, making them important for political or social events. To handle such massive databases, fundamental mathematical tools and algorithms related to centrality measures and network matrix properties are actively being developed. Indeed, the PageRank algorithm, which was initially at the basis of the development of the Google search engine  (Brin and Page, 1998; Langville and Meyer, 2006) , is directly linked to the mathematical properties of Markov chains  (Markov, 1906)  and Perron-Frobenius operators  (Brin and Stuck, 2002; Langville and Meyer, 2006).
In contrast to the Hermitian problem, the Google matrices of directed networks have complex eigenvalues. The only physical systems where similar matrices had been studied analytically and numerically correspond to models of quantum chaotic scattering whose spectrum is known to have such unusual properties as the fractal Weyl law  (Sjöstrand, 1990; Zworski, 1999; Nonnenmacher and Zworski, 2007; Shepelyansky, 2008; Gaspard, 2014). In this review we present an extensive analysis of a variety of Google matrices emerging from real networks in various sciences including the WWW of United Kingdom universities, Wikipedia, the Physical Review citation network, the Linux Kernel network, the world trade network (WTN) from the UN COMTRADE database, brain neural networks, networks of DNA sequences, and many others.
As an example, the Google matrix of the Wikipedia network of English articles (August 2009) is shown in Fig. 1. We demonstrate that the analysis of the spectrum and eigenstates of a Google matrix of a given network provides a detailed understanding about the information flow and ranking. We also show that such types of matrices naturally appear for Ulam networks of dynamical maps  (Shepelyansky and Zhirov, 2010a; Frahm and Shepelyansky, 2012a)  in the framework of the Ulam method  (Ulam, 1960). Currently, Wikipedia, a free online encyclopedia, stores more and more information and has become the largest database of human knowledge. In this respect it is similar to The Library of Babel, described by  Borges (1962) , and "The Library exists ab aeterno. " The understanding of hidden relations between various areas of knowledge on the basis of Wikipedia can be improved with the help of a Google matrix analysis of directed hyperlink networks of Wikipedia articles as described in this review.
Similar data for the network of the University of Cambridge (  2006 ) with N ¼ 212 710 and N l ¼ 2 015 265  (Frahm, Georgeot, and Shepelyansky, 2011) κ ¼ N X N i¼1 P(KðiÞ)P Ã (K Ã ðiÞ) -1: ð4Þ Even if all the networks from Fig. 6  have similar algebraic decay of PageRank probability with K and similar β ∼ 1 exponents we see that the correlations between PageRank and CheiRank vectors are drastically different in these networks. Thus the networks of UK universities and nine different language editions of Wikipedia have the correlator κ ∼ 1-8 while all other networks have κ ∼ 0. This means that there are significant differences hidden in the network architecture which are not visible from a PageRank analysis. We discuss The straight dashed lines show the power law fits for PageRank and CheiRank with the slopes β ¼ 0.
C. PageRank-CheiRank plane A more detailed characterization of correlations between PageRank and CheiRank vectors can be obtained from a distribution of network nodes on the two-dimensional plane (2D) of indices ðK; K Ã Þ. Two examples for Wikipedia and Linux networks are shown in Fig. 7. A qualitative difference between the two networks is obvious. For Wikipedia we have a maximum of density along the line ln K Ã ≈ 5 þ ðln KÞ=3 that results from a strong correlation between PageRank and CheiRank with κ ¼ 4. 08. In contrast to that for the Linux network V2. 4 we have a homogeneous density distribution of nodes along lines ln K Ã ¼ ln K þ const corresponding to uncorrelated probabilities PðKÞ and P Ã ðK Ã Þ and an even slightly negative value of κ ¼ -0. 034. Note that if for Wikipedia we generate nodes with independent probabilities distributions P and P Ã , obtained from this network at the corresponding value of N, then we obtain a homogeneous node distribution in the ðK; K Ã Þ plane [in the ðlog K; log K Ã Þ plane it takes a triangular form, see Fig.
VII. LINUX KERNEL NETWORKS Modern software codes now represent complex large-scale structures and analysis and optimization of their architecture become a challenge. An interesting approach to this problem, based on a directed network construction, was proposed by  Chepelianskii (2010). Here we present results obtained for such networks. A. Ranking of software architecture Following Chepelianskii (2010) we considered the procedure call networks (PCN) for open source programs with emphasis on the code of Linux Kernel (Linux, 2010a) written in the C-programming language  (Kernighan and Ritchie, 1978). In this language the code is structured as a sequence of procedures calling each other. Because of that feature the organization of a code can be naturally represented as a PCN, where each node represents a procedure and each directed link corresponds to a procedure call.
For the Linux source code such a directed network is built by its lexical scanning with the identification of all the defined procedures. For each of them a list keeps track of the procedure calls inside their definition. An example of the obtained network for a toy code with two procedures start_kernel and printk is shown in Fig. 15. The in and out degrees of this model, noted as k and k, are shown in Fig. 15. These numbers correspond to the number of outgoing and ingoing calls for each procedure. The obtained in-and outdegree probability distributions P in ðkÞ and P out ð kÞ are shown Fig. 15  for different Linux Kernel releases. These distributions are well described by power law dependences P in ðkÞ ∝ 1=k μ in and P out ð kÞ ∝ 1= kμ out with μ in ¼ 2. 0 AE 0. 02 and μ out ¼ 3. 0 AE 0. 1.
2 in However, in other type of networks both directions of links appear in a natural manner and thus both vectors of PageRank and CheiRank play an important and useful role. For the Linux Kernel network the correlator κ Eq. (  4 ) between PageRank and CheiRank vectors is close to zero (see Fig. 6 ). This confirms the independence of two vectors. The density distribution of nodes of the Linux Kernel network, shown in Fig. 7 (b), has a homogeneous distribution along ln K þ ln K Ã ¼ const lines demonstrating once more the absence of correlations between PðK i Þ and P Ã ðK i Ã Þ. Indeed, such homogeneous distributions appear if nodes are generated randomly with factorized probabilities P i P i Ã  (Chepelianskii, 2010; Zhirov, Zhirov, and Shepelyansky, 2010). Such a situation seems to be rather generic for software architecture.
Indeed, other open software codes also have a small value for a correlator, e. g. , OpenSource software including GIMP 2. 6. 8 has κ ¼ -0. 068 at N ¼ 17 540 and X Windows server R7. 1-1. 1. 0 has κ ¼ -0. 027 at N ¼ 14 887. In contrast to these software codes the Wikipedia networks have large values of κ and inhomogeneous distributions in ðK; K Ã Þ plane (see Figs. 6  and 7 ). The physical reasons for the absence of correlations between PðKÞ and P Ã ðK Ã Þ have been explained by  Chepelianskii (2010)  on the basis of the concept of "separation of concerns" in software architecture  (Dijkstra, 1982). It is argued that a good code should decrease the number of procedures that have high values of both PageRank and CheiRank since such procedures will play a critical role in error propagation since they are both popular and highly communicative at the same time.
4 even if a certain 20% increase of d appears for the latest Linux version V2. 6. We attribute this deviation for the version V2. 6 to the well-known fact that significant rearrangements in the Linux Kernel have been done after version  V2. 4 (Linux, 2010a). Thus in view of these restrictions we consider that there is rather good agreement of the fractal dimension obtained from the fractal Weyl law with d ≈ 1. 3 and the value obtained with the cluster growing method which gives an average d ≈ 1. 4. The fact that d is approximately the same for all versions up to V2. 4 means that the Linux Kernel is characterized by a selfsimilar fractal growth in time. The closeness of d to unity signifies that procedure calls are almost linearly ordered that corresponds to a good code organization. Of course, the fractal Weyl law gives the dimension d obtained during the time evolution of the network.
This dimension is not necessary the same as for a given version of the network of fixed size. However, one can expect that the growth goes in a self-similar way  (Dorogovtsev, Goltsev, and Mendes, 2008)  and that the static dimension is close to the dimension value emerging during the time evolution. This can be viewed as some kind of ergodicity conjecture. Our data show that this conjecture works with good accuracy up to the Linux Kernel V. 2. 6. Thus the results obtained by Ermann, Chepelianskii, and Shepelyansky (  2011 ) and described here confirm the validity of the fractal Weyl law for the Linux Kernel network with the exponent ν ≈ 0. 65 and the fractal dimension d ≈ 1. 3. It is important to note that the fractal Weyl exponent ν is not sensitive to the exponent β characterizing the decay of the PageRank.