dataesr/scientific-paragraphs-categorization huggingface dataset:
MR POTATOHEAD: Property Market Edition As described in greater detail in  Parker et al. (2019) , MR POTATOHEAD has recently been updated and modified to specifically describe land market models. The authors, along with many other collaborators, are working to develop a simple common programming code base for agentbased land market models, which will nest all of our models, implemented in different languages with slightly different assumptions, into a common code base. This code base will allow us to directly compare the implications of different models assumptions, as well as to easily create new hybrid model instances combining model assumptions. As a first step towards this goal, we held two international workshops, during which land-market-specific versions of the MR POTATOHEAD agent decision model, land exchange, and landscape classes were created.
MR POTATOHEAD computer model For the prototype programming model now in development, we are writing our model on the Netlogo platform proposed by Uri Wilensky  (Wilensky, 1999). Thus, our computer model relies on the NetLogo Design Pattern (cf. Figure  5 ). Although NetLogo has some technical limitations, it is more likely to be used by others, and contributed to, as a meta design pattern, than a code base in another language. We perceive it as having has relatively low barriers to entry, compared to other languages, providing an easy entry point for new modelers, especially those lacking prior computer science training. As discussed by  Railsback and Grimm (2019) , NetLogo can also be used for large-scale scientific applications. As it relies on the standard NetLogo pattern, the MR POTATOHEAD computer model should also be accessible to a majority of modelers in building ABM/LUCC computer simulations, and facilitate conceptual model cross-model comparisons for the modelers community working or not with NetLogo.
Conclusions In this paper, we have detailed a new generic multidisciplinary method to build ABM/LUCC of land and housing market aiming at simulate and evaluate territorial planning policies. Our method is based on both conceptual modeling and Design Patterns software engineering techniques. Design Patterns inspire all stages of the modeling process proposed and help to formulate efficient and lasting software solutions in multidisciplinary contexts. As example, we detail the construction of an ABM/LUCC from its conceptual model to its computer model counterpart. We use the MR POTATOHEAD Property Market Edition as a Conceptual Design Pattern for building a conceptual ABM/LUCC. The MR POTATOHEAD ontology provides a structured way to "decompose that system into the relevant parts, their properties, and the relationships between them" and to separate model design from model construction, as recommended by  Smaldino (2020) , line 303, as best practices for translation of verbal models to computer code.
Moreover, the method help us to bridge the gap between economic descriptions and computer model/programming model. The NetLogo software is used to implement the resulting programming model throw the generic NetLogo Design Pattern. Our method permits us to efficiently integrate the spatial aspect of ABM/LUCC. The preliminary results show the method facilitates the ABM/LUCC formulation and one-to-many and many-to-one relationships between physical land parcels and properties descriptions. The Netlogo design pattern helps in our particular case study that we had one team member who had a deep understanding of both economic and agent-based models, as well as rudimentary understanding of computer science and programming concepts. This method allowed us to build a prototype of the economic conceptual model on NetLogo providing a generic virtual laboratory.
We also think that this method could be of interest to the readers of this journal who are expanding models on biocomplexity in the environment to individual based modelling, as they would normally have a similar formal mathematical presentation of their models to the economics norm. This model will now be applied to Corsica (France), which represents an ideal framework for analysis because of its specificities: islands with a high expansion of population and tourists and high demand for accommodation. Policymakers could benefit from the empirical evidence provided by our study through the greater understanding of agents' behaviour and interactions. In a future work, we will employ real data in order to parametrize and calibrate the ABM/LUCC. The data (PERVAL) will come from an original dataset -filled by notaries -containing all real estate transactions in Corsica in last 15 years.
Acknowledgment This work supports one of the research themes of the  ESTATE  Project ( Etude de la SouTen-Abilité des recompositions TErritoriales de la Corse ) funded by the  Collectivity of Corsica (Cullettività di Corsica). We would like to thank the  University of Corsica  for funding the hosting of  Professor Dawn Cassandra Parker  as a visiting professor through its  International Visiting Chair program ; the  Waterloo Institute for Complexity and Innovation , the  University of Waterloo   International Research Partnership Grant programme , the  Oxford Institute for New Economic Thinking  for workshop support; and all participants in the 2018 model template development workshops.
This allows us to define the density of the NMF peaks in time across multiple channels. Moreover, DBSCAN executes very fast  [39, 40]  on our NMF time series (typically in less than 1 ms) and the algorithm does not need prior information about the number of points that are part of a cluster, or the number of clusters in the dataset. As a result, DBSCAN is suitable for multiple target detection in scenarios with multiple targets. We configured DBSCAN to seek arrivals detected in at least 70% of the array elements, and spaced in time no more than the maximum propagation delay between any two elements. This makes it possible to discard peaks that are not detected reliably by all elements, or that are separated by a large time delay, enough to suspect that they may correspond to different emissions from the environment, or to reflections from different targets.
Our emulation framework consists of a software written in Python. Here, we assume that the array of opportunity is located at a depth of 10 m within an isovelocity water body with flat surface and bottom having maximum depth of 100 m. (The lake experiment in Section 6 serves to test our algorithm in realistic propagation conditions, with a stratified medium and non-flat bottom. ) For the emulation scenario, we assume that the array insonifies the underwater environment by transmitting the chirp signal, and listens to reflections from the environment. We thus emulate a received signal by propagating the chirp to the target, and back to the array. In particular, we shift the phase of the chirp as a function of the location of the source and of the position of each acoustic array element. We then scale a noise sequence from the Monte Carlo set to yield a desired SNR level, and superimpose the received signal to the noise.
Equipment and Software The acoustic array we deployed for this experiment is a part of the opto-acoustic system built in the scope of SYMBIOSIS project  [41]  for the non-invasive monitoring of coastal and deep waters. The purpose of the platform is to detect, localize, and monitor fish stock from different target pelagic fish species, using a chain of acoustic and optical detection systems and algorithms. Figure  6a  shows a rendering of the acoustic components in the upper portion of the SYMBIOSIS instrumentation. The top and bottom cylinders host control hardware and batteries to operate the platform, whereas the two cylindrical pieces of equipment facing right constitute the acoustic array considered in our experiment. Each cylinder contains a software-defined ultra-short baseline (USBL) with modem capabilities (SDM-USBL).
We measured the accuracy of the sync-in signal to be on the order of 200 ns, which is sufficiently accurate, given the sampling frequency of 62. 5 kHz. With reference to the scheme in Figure  7 , we control the equipment from the laptop ashore (see also Figure  8b ) using custom Python software. Once we issue the sync-in signal, the Jetson board collects data in real time, and uploads them to the laptop ashore. The software then processes the acoustic time series through the procedure of Section 3 to obtain DoA estimates.
Experiment Setup We performed our experiment on 13 June 2019. The weather remained mostly sunny throughout the day, with little wind. The water temperature ranged from 19. 4 • C at the bottom, up to 21. 5 • C near the lake's surface. Figure  9  sketches the deployment configuration: we lowered the SYMBIOSIS unit in the water near the jetty of the Werbellin lake marina (Figure  8a ), and placed it on the lake bottom at a depth of 7. 5 m, so that the two units that constitute the array of opportunity remained submerged at a depth of 6. 7 m and 7. 1 m, respectively. The red arrow in Figure  9  denotes the location of the equipment and the reference (i. e. , 0 • ) direction of the acoustic array. Additionally, we deployed a small motorboat carrying an active target, namely a software-defined modem emitting linear chirp signals with a duration of 10 ms in the 7-17 kHz band.
We constructed plot-based matrices of species abundances (abundance-matrix hereafter) for plants and for birds to represent local species assemblages. For this, we estimated the absolute abundance of each species based on the cumulative number of records (fruits or birds) per plot, pooling the data for the two annual fruiting seasons. Species rarefaction curves (Fig. S7 ) indicate that our sampling effort was complete enough to detect maximum species richness in both plants and birds.
Phylogenetic diversity-We extracted the phylogenetic distances between the plant species studied here from the comprehensively dated phylogeny of the European flora "Daphne"  (Durka & Michalski, 2012). This phylogeny is based on the backbone family phylogeny of the Angiosperm Phylogeny group III  (Bremer et al. , 2009) , but dated using more recent molecular studies. We extracted the plant tree using the drop. tip function of the APE package  (Paradis & Schliep, 2018)  in R. For birds, we built 10000 time-calibrated trees from the BirdTree online tool (BirdTree. org), using Ericson method as a backbone for phylogenetic reconstruction  (Ericson et al. , 2006; Jetz, Thomas, Joy, Hartmann, & Mooers, 2012). We used TreeAnnotator  (Drummond & Rambaut, 2007)  to generate the maximum clade credibility (Bayesian MCC) tree.
For this, we first combined all matching traits to build plant and bird multidimensional trait spaces, based on Euclidean distances, using Principal Coordinates Analysis (PCoA;  Villéger et al. , 2008) , in which all species (i. e. the whole set of species from every local assemblage) were projected. Then, we calculated values of Functional Dispersion (FDis) separately for plants and for birds for each plot. This index reflects the average distance of the species present locally to the centroid of the multidimensional trait-space of the species assemblage  (Laliberté & Legendre, 2010; Villéger et al. , 2008). Abundance-weighted FDis values and PCoA axes were estimated with the dbFD function in the R package FD  (Laliberté, Legendre, & Shipley, 2014).
ACKNOWLEDGEMENTS We thank  Carlos Guardado  and  Juan Rodríguez  for technical support. The research was funded by  MinECo/FEDER  grants  CGL2011-28430  and  CGL2015-68963-C2-2-R  to D. G. , and  BES2012-052863  and  BES-2016-078260  to I. D. and R. P. , respectively. I. D. is currently funded by the  Alexander von Humboldt Foundation. Fieldwork was conducted with the permission of the  Government of Asturias. Authors' contributions: D. G. , M. S. and R. P. conceived and designed the study; D. G. , J. R-P. and I. D. collected field data; R. P. and M. S. collected phylogenetic and trait data and performed analyses; R. P. and D. G. wrote the manuscript; all authors reviewed the manuscript and approved the final version.
To this end, we study the nestedness metrics using the following procedure: for each of the 199 real bipartite networks of our dataset, we build the corresponding maximum entropy and maximum likelihood ensemble that preserves on average the observed degree sequence  [30, 18]. We then measure nestedness in the ensemble built for each of the real networks according to each of the metrics and we compare the results with the corresponding nestedness value of the observed network. Secondly, we perform various statistical analyses to determine the relation of each metrics with network properties, like size, fill, degree degeneracy, etc. The rest of the paper is organized as follows: in section 2, we briefly describe each of the metrics scrutinized in this work. Our results are presented in section 3 together with a thorough comparison among the different metrics.
More details about the calculations done and the methods employed are provided in section 4, which is followed by a general discussion in section 5. The metrics are reviewed and analyzed independently, so that a reader interested in a particular metrics may directly browse through its dedicated subsections. Moreover, as explained in section 4, a working package called nullnest is provided, which contains: a) the probability matrices of contacts among the species allowing to reconstruct the maximum entropy, maximum likelihood null model of the studied real networks, b) the documented program that produces this probability matrix to be used with other real matrices, c) the documented programs that calculate all the indices on the real and randomized matrices.
Here we used the implementation of the popular bipartite package  [26]  where the null model used to calculate d rand keeps constant size and fill (see Section 4 for details). • The nestedness metrics based on overlap and decreasing fill (NODF)  [25]. This index measures the average percentage of shared contacts between pairs of rows which present a decreasing degree ordering in their maximally packed form (idem for columns). Therefore, this metrics separately informs on the contribution of rows and of columns to the observed nestedness. It should be noted that the overlaps between all the possible pairs of rows (columns) are only taken into account if the considered pair is ordered in decreasing degree, otherwise it assigns a null value to the overlap. For this metrics, the higher the NODF index, the more nested the system is.
Significance of nestedness of empirical networks. We have measured the nestedness of 191 empirical ecological networks extracted from the Web of Life  [43]  as well as 8 economic networks which represent the trading interactions between the buyers and the sellers of two different fish markets  [16]. To compare the average nestedness over the ensemble with that corresponding to empirical networks, we have used the six metrics described above plus two variations (the stable NODF and the normalized spectral radius). The details about the networks dataset can be found in section 4. As it has been shown analytically and numerically (using NODF and the spectral radius) in  [18] , the nested structure of mutualistic networks is a consequence of the double heterogeneity in the degree sequence which results from entropic effects.
Influence of network properties on the behavior of the different metrics The results presented in Fig. 4  reveal that the metrics studied behave in different ways under the same null model, showing distinct levels of fluctuations and sometimes a systematic bias, as it is the case for the discrepancy and temperature indices. This finding suggests that the different algorithms implemented by each metrics may eventually translate into non-equivalent nestedness measures. We explore further this situation in Fig. 5 , where we compare the values of nestedness obtained for a group of mutualistic networks when measured using each of the metrics. As it can be observed, for the same dataset not only the value of nestedness itself but also the ranking of the networks according to their degree of nestedness is strongly metrics' dependent.
Ideally, as it has been recalled by several authors  [5, 23, 25] , a well-behaved nestedness metrics ought to be independent of the particular network parameters and, furthermore, rank the degree of nestedness of a given set of networks universally. The results discussed above put in evidence that the second condition is not always true. Regarding the first requirement, we next explore more carefully how the nestedness values given by each metrics depend on the network parameters. In particular, since the networks of the dataset cover a wide range of parameter values (see Fig. 5  for an example), we analyze the effects of three characteristic network properties: size, density of links and eccentricity. These quantities are defined as follows: Triangle symbols stand for small networks (less than 50 nodes), circles for medium size networks (more than 50 nodes and less than 410) and squares for large networks (more than 410 nodes).
Dataset Our study has been carried out using a large dataset composed by a total of 199 bipartite networks, including both ecological and economic interactions. The ecological networks were extracted from the Web of Life public repository  [43] , which contains the following data: • 118 Plant-pollinator networks. Here, the links among guilds represent mutualistic relationships, characterized by benefiting both interacting agents. In this case, animals, including mainly insects, pollinate flowering plants. This activity provides the pollinators with nutrients while pollinated plants enhance its reproductive success. The two set of nodes of the bipartite network represent the species of the plant and pollinator guilds. • 23 Seed-disperser networks. Here, the links also represent, mutualistic interactions, consisting now of birds feeding on the fruits of certain plants and then contributing to their reproduction and dispersal by disseminating their seeds.
These networks include two examples of diverse types of communities: a network depicting ants which feed on plant nectar, this being a consumer-resource interaction, and two networks representing communities where ant species live in a mutualistic association with certain plant species known as Myrmecophytes. On the other hand, our dataset includes a number of economic networks which are publicly available in  [44] , consisting of: • 8 economic networks representing buyers-sellers interactions in the Boulogne-sur-Mer Fish Market in France  [16]. These are mutualistic networks taken from a very different context. Each network describes the transactions observed in different days in the bilateral or in the auction Fish Market. These daily networks are typically much denser than ecological ones.
Generation of a sample of null networks In order to maximize the entropy under the constraint of keeping the average degree sequence fixed and equal to the observed one, it is necessary to apply the Lagrange Multipliers' technique. The determination of the value of such multipliers, obtained by maximizing the likelihood that the empirical degree sequence appears with maximum probability in the random ensemble, provides, as aforementioned, the probability of interaction among species or agents from different guilds. This probabilistic bipartite matrix can then be used to sample the random ensemble of networks. For each real network in our dataset, we sampled 10 4 null networks with the obtained probability interaction matrix. Across the same sample, each of these null matrices may vary in its size (number of connected nodes), density of links, degree sequence, redundancy of degrees or bipartite matrix excentricity.
Temperature We calculated the temperature metrics using the R software  [45]  and, specifically, the bipartite package  [26, 46]  version 1. 13. 0. In particular, we used the nested function and we set as method the binmatnest2 option. This calculates the temperature metrics by using an implementation by Jari Oksanen  [47]  of the binmatnest program by Miguel Rodriguez-Girones  [13]. We have redefined the resulting temperature, as shown in eq. (  6 ) in order to uniform the interpretation of the values yielded by all the metrics such that the higher the value of the corresponding index, the higher the nestedness.
Discrepancy We computed the discrepancy metrics using the R software  [45]  and the bipartite package  [26, 46]  version 1. 13. 0. In particular, we performed the calculation using the method discrepancy from the nested function. The final nestedness value measured is directly proportional to the density of links and size of the network. With the aim of removing such dependencies, we divided the resulting value of the metrics by the total numbers of links. This results in a relative discrepancy. Once again, we finally applied Eq. (  8 ) to obtain the same monotonic variation for all the indices.
Spectral radius We computed the largest eigenvalue using the R software  [45] , in particular the eigs_sym function from the rARPACK package  [49]. In order to calculate the normalized version of this metrics, we need an estimation of the largest spectral radius of a perfectly nested network of the same size and fill. To estimate each of these values, for each real network in our dataset we produced 100 new networks, characterized by being perfectly nested. These networks were generated using the SNM algorithm  [36] , which preserves the number of connected nodes (network size) and links, but modifies the pattern of connections and the degree sequences. This algorithm is divided into two procedures. First, the real network is randomized preserving only the fill and the size (that this, ensuring that every node has at least one connection).
Statistical calculations The statistical correlations were numerically calculated using Python. The Spearman rank correlation coefficient r s and its p-value were calculated using the Scipy package  [50] , in particular the scipy. stats. spearmanr function. We performed the linear fits using the Statsmodels package  [51] , which carries out a multilinear least-square regression and provides multiple information, including the adjusted R 2 , the partial regression coefficients, their standard deviation and their associated p-value. The t-ratio i,j corresponding to each partial regression coefficient, β i,j , is calculated as follows: t -ratio i,j = β i,j σ i,j (19) where σ i,j is the standard deviation associated to that coefficient. This index provides, hence, information on how significantly different from zero is a certain regression coefficient.
The nullnest repository Additionally to the comparative study here presented, we provide an open github repository named nullnest that aims at being a practical tool, for both ecologists and network scientist, to assess the nestedness of real and null networks. The repository is thoroughly documented, with examples and ready-to-use programs, and allows performing the analysis discussed in the present paper as well as some of the main calculations derived in  [18]. The nullnest repository is divided into two main blocks. The first part is related to the construction of the null model. In particular, we give a program to compute the null model discussed in section 2. 2 for any bipartite network introduced by the user. We also provide the ready-to-use probabilities of interaction in the null ensemble, for the whole dataset of empirical networks described in section 4.