dataesr/scientific-paragraphs-categorization huggingface dataset:
Summary of STM Applications The cases described above suggest that major efforts to develop STMs have taken different trajectories following the introduction of the concept in 1989. In Australia and Argentina, initial enthusiasm and progress was not sustained due to limitations in the data available to develop STMs, the dearth of land classification systems as a basis for STMs, and lack of resources and incentives for scientists and managers. In the USA, these limitations were overcome to varying degrees by the linkage of STMs to rangeland evaluation systems and financial assistance programs supported by government agencies. The vast scientific and administrative infrastructure provided by well-funded US government agencies has supported the nationwide development of numerous STMs. While this strategy has dramatically accelerated STM development compared to Australia and Argentina, it also introduced logistical difficulties associated with managing such a large number of STMs.
All three of these processes begin with a draft graphical model that serves as the basis for initial discussions and feedback from the group. Johanson and Fernandez-Gimenez (2015)  drew on these experiences together with those of participants in 16 collaborative ESD and STM development projects in the USA to identify common outcomes, challenges, and keys to success. Most efforts were successful in producing an STM or portion of an ESD. Additional outputs included publications, applications of the models to management, workshops, and databases. Many benefits beyond these tangible outputs were also identified, such as improved working relationships and communication among participants from different organizations, decreased conflict, increased efficiency of STM development, greater use of STMs, and improved data credibility.
The study by Herrero et al. (  2013 ) is a first approximation to spatially disaggregate a global livestock dataset to assist in determinations of greenhouse gas emissions at regional to local scales. Most of the developed countries have low greenhouse gas emission intensities due to improved and intensive feeding practices, and higher feed quality; conversely, sub-Saharan Africa is a hot spot for high greenhouse gas emission intensities as a result of low-quality feeds and animals with low productive potential animal -1  (Herrero et al. 2013). Adverse environmental effects are minimized by improving productivity in the metrics of carcass weight and growth rate  (Capper and Hayes 2012; White and Capper 2013 ). As productivity increases, the proportion of daily energy allocated to maintenance decreases and maintenance requirements of the total animal population decreases.
Development of Disease Ecology as a Discipline One of the key developments in disease ecology was when  May and Anderson (1979)  explored how disease dynamics might change when the host population is considered to be dynamic rather than assumed to be constant. Then, depending on the specifics of how the parasite is transmitted, disease can emerge as a strong factor regulating the host population. This development probably sounds obvious to many ecologists, but at that time much of disease ecology was borrowed from studies on humans, in less variable populations. Many principles of wildlife disease ecology continue to be derived from human systems, where the datasets tend to be richer  (Grenfell et al. 2002). However, there are several important ways in which wildlife populations are likely to differ from human or livestock populations: wildlife populations fluctuate more; reproduction is more closely tied to resources and population density; movements can be more localized; and predation can interact with disease.
2014). As part of the goal to bring agencies together in their management of rangelands, the USDA Natural Resources Conservation Service, USFS, and BLM entered into a Memorandum of Understanding in 2005 to define and describe rangelands using a standard classification system, ecological sites  (Caudle et al. 2013). This resulted in federal and non-federal rangelands being classified using the same process where soils, landforms, and climate describe potential plant communities and their production. This led to common terminology and similar metrics for determining rangeland status and trends. Standardized terms and metrics position land managers to take advantage of new technology that cross-cuts management and political boundaries through remote sensing and database access (Chap. 9, this volume).
Even for rangelands managed by a single agency and land use, the BLM used five types of data (frequency, cover, production, utilization, and photos) from 15 different methods to monitor range trend plots across 310 allotments in six states  (Veblen et al. 2014). A consistent, minimal (i. e. , core) set of standard indicators and methods for rangeland monitoring offers several potential advantages. First, it provides the ability to combine datasets from different monitoring programs (e. g. , across jurisdictions). Second, it allows for data to be scaled up to support inferences to areas of larger extent. Third, standard indicators give an opportunity for data to be reused for different purposes. For example, when a new management concern arises, existing   Miller et al. (2010). General structure follows  Bestelmeyer et al.
Seemingly minor differences among methods can result in incompatible data  (Bonham 2013). For instance, differences in definitions between soil and rock can cause one method to produce higher estimates of an exposed soil indicator relative to another method. Similarly, for plant-cover indicators, foliar cover methods usually yield different estimates than total-canopy cover methods  (Toevs et al. 2011). Core methods represent a minimal set of information that should be collected in almost all monitoring efforts and are intended to encourage combination and "scaling-up" of monitoring datasets. In selecting core methods, the repeatability, quality, and objectivity of data that the method provides should be considered. The attributes of a method that should be.
Uncertainty estimates (often expressed as a standard error or confidence interval, or used as part of a statistical test) are not direct measures of the variability of an indicator, but a reflection of how close an indicator estimate is to the actual indicator value in the study area. Table  16. 3 Descriptions of the basic components of a sampling design following the definitions of  Thompson (2002)  and  Lohr (2009)  In a statistically valid sample, the area that each sample location represents can be quantified. It then becomes possible to "scale up" monitoring results to other reporting units or to combine similar monitoring datasets to make better predictions for a monitoring area (Fig. 16. 2). Note that a statistical sampling design and consistent indicators and methods are necessary for this to be possible  (Toevs et al.
Summary The past 25 years has seen a rapid increase in the uses of and disturbances to rangelands that has taxed our ability to monitor these ecosystems with traditional techniques. However, the same time period has brought about conceptual developments that lay a foundation for more effective, and ultimately more efficient, rangeland monitoring. The cornerstones of this foundation are an understanding the nonlinear, cross-scale dynamics of rangeland ecosystems, and the development of functional indicators of critical ecosystem processes. The standardization of indicators (and measurement methods) to the extent possible and adoption of statistically valid and scalable sampling designs will help ensure monitoring data that not only satisfies initial objectives, but also is able to be combined with other datasets and analyzed for other or larger scale purposes.
g. ,  Luscier et al. 2006; Laliberte et al. 2012a; Hulet et al. 2013 Hulet et al. , 2014a) )  and coarse  (Laliberte et al. 2006)  scales. Karl (2010)  used OBIA in predictions of three rangeland indicators in a southern Idaho study area. Laliberte et al. (2004)  used an OBIA method to detect shrub encroachment in southern New Mexico rangelands using historic aerial and current high-resolution satellite imagery. Several satellite sensors (e. g. , AVHRR, Landsat, MODIS) have now been operational long enough to provide a reliable record of change in rangeland ecosystems. For example,  Homer et al. (2004)  developed a National Land Cover Dataset for the United States from 2001 Landsat imagery that has been subsequently updated to assess change over time  (Xian and Homer 2010; Jin et al. 2013).
It is an inefficient approach if a separate monitoring program is needed for each management objective. Additionally, monitoring programs designed tightly around a single objective (e. g. , monitoring of grazing use impacts) may be inappropriate for informing other management objectives. Overall efficiency of monitoring programs increases as the number of questions that can be answered with a dataset increases, and if data from multiple efforts can be combined. This is because monitoring data that have been collected for one purpose can be reused to answer other objectives. For example, data collected since 2004 (NRCS) and 2011 (BLM) by the National Resource Inventory (NRI) program are designed to generate regional and national estimates of rangeland status and trend. As of 2015, many of these data were being used to support the development and revision of ecological site descriptions.
In the future, it is anticipated that they will be combined with local to regional monitoring data to address more specific questions, such as habitat suitability for wildlife species or for use in developing remote-sensing products provided restrictions regarding confidentiality of data can be overcome. Combining datasets is only possible, however, if indicators and methods are consistent between programs and if the datasets were based on a probabilistic sampling design. Although combining different datasets is not always straightforward statistically (e. g. , for monitoring over large landscapes or when sample size requirements are high due to variability in an indicator), it can be worth the effort.
Cross-Jurisdictional Monitoring Another benefit of the recent adoption of core indicators and methods is that it opens up new opportunities for monitoring conditions across boundaries of ownership or administration. Land owners or agencies that use core methods and statistically based sampling designs can interchange and combine datasets to address management questions related to the context of a management unit in a larger landscape or pertaining to resources or disturbances that cross boundaries. This can be particularly useful in rangelands where ownership is fragmented, and can be applied at all scales from local to national. For example, monitoring the status and condition of seasonal habitats is necessary for the conservation of greater sage-grouse (Centrocercus urophasiuanus) across its range.
Approximately 52 % of the current greater sage-grouse distribution occurs on land managed by the BLM, whereas the remainder is managed by private (31 %) or other (17 %) federal and state agencies (U. S. Fish and Wildlife Service 2010). The mixed land ownership has resulted in multiple sage-grouse habitat mon- itoring programs. Knowledge of the condition or trend of greater sage-grouse on lands managed by one agency or land owner may be informative for some questions relative to the affairs of that organization. However it provides an incomplete picture of habitat for the species as a whole. Because the BLM Landscape Monitoring Framework and the NRCS National Resources Inventory both adopted the core indicators and methods, these datasets have been combined to produce estimates of greater sage-grouse habitat indicators across private, tribal, and BLM lands from a sample of over 6500 locations between 2011 and 2013 (Fig.
This advance translated to a shift in thinking from monitoring plant community responses to land uses to monitoring changes in land health. The adoption of conceptual models as a mechanism for documenting and illustrating how ecological processes, disturbances, and management affect an ecosystem, has contributed to the identification and selection of functional ecosystem indicators. Conceptual models not only identify what parts of an ecosystem should be monitored, they provide insight into how monitoring data should be interpreted and used for making management decisions. Differences in indicators and measurement methods among monitoring programs has hindered the ability of data to be used for multiple objectives or combined to understand conditions over larger scales. A consistent core set of standard indicators and methods for rangeland monitoring provides the ability to combine datasets from different monitoring efforts, allows data to be scaled up to larger extents, and expands opportunities to reuse data for other purposes.
Acknowledgements  Jeff Herrick ,  Mitch McClaran ,  Robin Reid , and  Nathan Sayre  provided insightful comments that improved clarity and enhanced chapter content. Layne Coppock  provided a perspective that enriched the chapter in both depth and breadth. Nathan Sayre  graciously provided prepublication content of his book that was in press at the time this chapter was written. Joeanna Brooks  redrew Fig. 1. 1 and created Fig. 1. 3. Acknowledgments We thank  David Briske  for his vision and efforts in organizing this volume and his input on earlier drafts of this chapter. This effort was supported, in part, by  NSF -DEB-1413900 ,  -DEB-0531691 ,  -DEB-1235828 , and  -DEB-1557262 ;  USDA   NIFA-2015-67019-23314 ; the  Bureau of Land Management ; the  Arizona Game and Fish Department ; the  Audubon Appleton-Whittell Research Ranch ; and the  Arizona Agricultural Experimentation  Project  ARZT-1360540-H12-199.
Acknowledgements We thank  David Briske ,  Ian Watson , and an anonymous reviewer for comments that substantially improved this chapter. B. T. B. was supported by appropriated funds to the  USDA Agricultural Research Service  and by the  Jornada Basin LTER program  ( DEB-1235828 ). M. E. F. -G. was supported by  Colorado Agricultural Experiment Station  Project  COL00698A  and  NRCS   Conservation Innovation Grant  Agreement No. 69-3A75-12-213. Acknowledgements This research was supported in part by an  NSF   IGERT  grant,  DGE-0903469 , and the  August T. Larsson Foundation (NJ Faculty, SLU ). Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the  NSF. The  Nebraska Cooperative Fish and Wildlife Research Unit  is jointly supported by a cooperative agreement between the  United States Geological Survey , the  Nebraska Game and Parks Commission , the  University of Nebraska-Lincoln , the  United States Fish and Wildlife Service , and the  Wildlife Management Institute.
C. R. Allen et al. Acknowledgements Authors want to thank  Courtney Currier  for her support during the course of this project, which received financial support from the  US National Science Foundation LTER   DEB 1235828. 14  Rangeland Ecosystem Services: Nature's Supply and Humans' Demand  Acknowledgments This work was funded in part by the  U. S. Bureau of Land Management and U. S. Geological Survey Coordinated Intermountain Restoration Project. The use of any trade, product, or firm name is for descriptive purposes only and does not imply endorsement by the  U. S. Government.
Acknowledgements The coauthors appreciate the various agencies and other donors-too numerous to mention here-that have supported their research and outreach activities. The coauthors are also grateful for the colleagues, graduate students, field staff, and members of pastoral and agropastoral communities who directly contributed to the findings reviewed in this chapter. Tracy Jones  is thanked for her editorial assistance on the manuscript. Finally, the coauthors thank  David Briske  and two peer reviewers for their constructive comments on earlier versions of this material. D. L. Coppock  et al.
More specifically, we discuss how a designer can perform design space exploration and develop FPGA solutions that can handle the various intricacies of LDPC codes, such as complexity of decoding algorithms for both binary and non-binary LDPC codes, regularity of code structure, parallel-exposure of the decoding schedules and Turbo-decoding messagepassing (TDMP) or two-phased message-passing (TPMP). We demonstrate how users without specialized hardware design expertise can easily develop LDPC decoders using three state of the art HLS tools: Altera OpenCL, Max-Compiler and Xilinx Vivado HLS. Moreover, the proposed decoder designs produced by the HLS tools achieve performances that are comparable to those of RTL designs. The main contributions of this paper can be summarized as follows: • identification of the key challenges in using HLS approaches for designing distinct LDPC decoder architectures; • assessment of the attainable LDPC decoding performance (i.
A. WIDE-PIPELINE ARCHITECTURE The wide-pipeline architecture in this study is modeled using the Altera OpenCL HLS infrastructure  [16]. The OpenCL is a parallel programming model that defines specific memory hierarchies and makes it easy for the users to express parallelism in the algorithm. Altera's HLS tool generates kernels from OpenCL code and interconnects them within a template architecture, shown in Figure  4a ). Therefore, only a part of.
C. LOOP-ANNOTATED ARCHITECTURE Vivado provides a HLS toolchain that enables users to design hardware from C/C++ or SystemC description  [21]. In addition to this description, the user can provide additional inputs, such as inline compiler directives using the #pragma construct or using a separate TCL script, to further optimize the generated hardware. The HLS tool compiles the inputs and synthesizes a hardware module, in the IP-XACT format, which implements the computation inside a top-level function that is marked within the user description. During this C-synthesis, all the functions, logic and arithmetic, inside this top-level are mapped onto hardware primitives. At this stage, the behavior of the generated design can be analyzed for functional correctness at the clock cycle level. Then, the generated hardware module is connected within a larger platform design, such as the one shown in Figure  4c ), before performing the circuit synthesis.
1) LOOP-ACCELERATION Although we consider non-binary decoding case here, the binary decoding case can be derived from it by trimming out some of the computation. Figure  7  a) shows the loop-structure for the FFT-SPA non-binary LDPC decoding algorithm. The trip count of each loop and its relative position in the nested-loop structure would determine what optimizations are applied on it produce an efficient design using Vivado HLS. We can use the loop-pipelining and loopunrolling directives to improve the parallelism in the loop computation. However, the effective parallelism in the design will depend on other factors, such as having enough bandwidth to serve data to all the parallel computation in the datapath  [25]. To optimize loop structures, we can use loop-unrolling to either unroll it completely and perform all its operations simultaneously, or unroll it only by a factor of k unroll , FIFO output is streamed to arith.
A. APPARATUS AND DATASET The details of Virtex and Stratix FPGA families utilized in this work are detailed in Table  2. The HLS-based LDPC decoders were developed and analyzed for codes dataset defined in Table  3. These codes were chosen due to their codeblock length, regularity (QC-LDPC and LDPC-IRA), and applicability-the codes used are Wi-Fi and   DVB -satellite 2 nd gen. (DVB-S2) standard codes. The place and route (P&R) results obtained for the logic utilization, operating clock frequency, decoding figures of merit, decoding throughput and decoding latency (for 10 decoding iterations) are summarized in Table  4. The parallelism nomenclature introduced earlier can be summarized also in terms of a number of processing units (PUs), where PUs = k dec ×k CUs.
31. Similar to described in lines: 5 to 13, but in the opposite direction of Tanner Graph traversal  (5)  32. FWHT kernel (execute 20:29) We employed the multi-kernel approach to design a decoder for the MSA decoding algorithm and used the dataset II to evaluate it. As shown in Table  4 , we also instantiated multiple CUs, however, due the higher resource requirements of the design, we were only able to instantiate up to 2 CUs. As noted earlier, the complexity of the design resulted in a reduction in the maximum operating clock frequency from 240MHz to 157MHz. This reduction in the clock frequency hampered the decoding performance of this decoder design.
D. DATAFLOW DECODER To evaluate the dataflow approach saw a M -modulo decoder based on designs optimized for LDPC-IRA codes  [6]  and benchmarked using dataset I (c. f. Table  3 ). This design can scale up or down based on the M FUs that are related to the Tanner graph regularity factor. In the dataset I, the normal frame DVB-S2 codes are expanded by a factor of 360 allowing the number of instantiated FUs to be any sub-multiple of it, M ∈{2 i ×3 j ×5 k }, with 0≤i≤3, 0≤j≤2, 0≤k≤1. In the benchmarked design, we studied sub-multiple factorizations of M ∈{45, 90, 180, 360} FUs, and instantiated multiple decoders based on the logic resources available in the FPGA chip (detailed in Table  2 ). In fact, a maximum of 720 FUs have been instantiated for up to k dec ∈{1, 2, 4, 8} decoders. As seen in Table  4 , a low number of P FUs leads to lower logic utilization and higher clock frequencies of operation.
VI. RELATED WORK AND DISCUSSION We compare the proposed decoders with an exaustive survey of RTL-based FPGA LDPC decoders in the literature  [4]. To provide a fair comparison across the HLS and RTL implementations, data concerning both logic utilization, number of PUs and the edges in the LDPC code is provided in Figures 9, 10, and 11. Therein, we plot our proposed HLS-based decoders 3 with the RTL-based ones. For other LDPC decoders in the binary domain, we utilized the dataset available in the survey  [4] , while for the non-binary case, we surveyed a dataset from the literature  [27] -  [34].
1) HIGH-LEVEL SYNTHESIS STATUS According to the defined taxonomy, HLS tools are currently in their third generation  [35]. Most, are C-based efforts, led by academia and industry. BlueSpec, is SystemVerilog-based tool for both FPGA and application-specific integrated circuit (ASIC) design  [36] , extending the FSM through guarded atomic actions. LegUp is a C-based tool, which generates an accelerator system from a C specification, separating data management and control into a MIPS processor from computation that occurs in the circuits  [37]. Another academia tool is the ROCCC, providing a C to VHDL compilation tool  [38]. Moreover, Cadence C-to-silicon, uses SystemC to raise the abstraction level and introduces transaction level models, targeting both FPGA and ASIC accelerators  [39]. OpenCL models are also been getting traction from both industry and academia alike, mainly due to the fact that an existing code base can be ported without syntax modifications from a CPU or GPU architecture onto reconfigurable circuits.
This work was supported in part by  Fundação para a Ciência e a Tecnologia  through the  Doctoral Scholarship under Grant   SFRH/BD/78238/2011 , in part by the  Instituto de Telecomunicações  under Grant  UID/EEA/50008/2013 , and in part by  INESC-ID  under Grant  UID/CEC/50021/2013.