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  • 1
    Publication Date: 2022-12-10
    Description: Forests mitigate climate change by storing carbon and reducing emissions via substitution effects of wood products. Additionally, they provide many other important ecosystem services (ESs), but are vulnerable to climate change; therefore, adaptation is necessary. Climate‐smart forestry combines mitigation with adaptation, whilst facilitating the provision of many ESs. This is particularly challenging due to large uncertainties about future climate. Here, we combined ecosystem modeling with robust multi‐criteria optimization to assess how the provision of various ESs (climate change mitigation, timber provision, local cooling, water availability, and biodiversity habitat) can be guaranteed under a broad range of climate futures across Europe. Our optimized portfolios contain 29% unmanaged forests, and implicate a successive conversion of 34% of coniferous to broad‐leaved forests (11% vice versa). Coppices practically vanish from Southern Europe, mainly due to their high water requirement. We find the high shares of unmanaged forests necessary to keep European forests a carbon sink while broad‐leaved and unmanaged forests contribute to local cooling through biogeophysical effects. Unmanaged forests also pose the largest benefit for biodiversity habitat. However, the increased shares of unmanaged and broad‐leaved forests lead to reductions in harvests. This raises the question of how to meet increasing wood demands without transferring ecological impacts elsewhere or enhancing the dependence on more carbon‐intensive industries. Furthermore, the mitigation potential of forests depends on assumptions about the decarbonization of other industries and is consequently crucially dependent on the emission scenario. Our findings highlight that trade‐offs must be assessed when developing concrete strategies for climate‐smart forestry.
    Description: Plain Language Summary: Forests help mitigate climate change by storing carbon and via avoided emissions when wood products replace more carbon‐intensive materials. At the same time, forests provide many other “ecosystem services (ESs)” to society. For example, they provide timber, habitat for various species, and they cool their surrounding regions. They are, however, also vulnerable to ongoing climate change. Forest management must consider all these aspects, which is particularly challenging considering the uncertainty about future climate. Here, we propose how this may be tackled by computing optimized forest management portfolios for Europe for a broad range of future climate pathways. Our results show that changes to forest composition are necessary. In particular, increased shares of unmanaged and broad‐leaved forests are beneficial for numerous ESs. However, these increased shares also lead to decreases in harvest rates, posing a conflict between wood supply and demand. We further show that the mitigation potential of forests strongly depends on how carbon‐intensive the replaced materials are. Consequently, should these materials become “greener” due to new technologies, the importance of wood products in terms of climate change mitigation decreases. Our study highlights that we cannot optimize every aspect, but that trade‐offs between ESs need to be made.
    Description: Key Points: Strategies for climate‐smart forestry under a range of climate scenarios always lead to trade‐offs between different ecosystem services (ESs). Higher shares of unmanaged and broad‐leaved forests are beneficial for numerous ESs, but lead to decreased timber provision. The mitigation potential of forests strongly relies on substitution effects which depend on the carbon‐intensity of the alternative products.
    Description: European Forest Institute (EFI) Networking Fund http://dx.doi.org/10.13039/501100013942
    Description: Bayerisches Staatsministerium für Wissenschaft und Kunst, Bayerisches Netzwerk für Klimaforschung (BayKliF) http://dx.doi.org/10.13039/501100004563
    Description: Swedish Research Council Formas
    Description: German Federal Office for Agriculture and Food (BLE)
    Description: https://doi.org/10.5281/zenodo.6667489
    Description: https://doi.org/10.5281/zenodo.6612953
    Keywords: ddc:634.9 ; forest management ; climate change mitigation ; substitution effects ; climate‐smart forestry ; ecosystem services ; robust optimization
    Language: English
    Type: doc-type:article
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  • 2
    Publication Date: 2019-06-28
    Description: Two key issues for induction algorithms are the accuracy of the learned hypothesis and the computational resources consumed in inducing that hypothesis. One of the most promising ways to improve performance along both dimensions is to make use of additional knowledge. Multi-strategy learning algorithms tackle this problem by employing several strategies for handling different kinds of knowledge in different ways. However, integrating knowledge into an induction algorithm can be difficult when the new knowledge differs significantly from the knowledge the algorithm already uses. In many cases the algorithm must be rewritten. This paper presents Knowledge Integration framework for Induction (KII), a KII, that provides a uniform mechanism for integrating knowledge into induction. In theory, arbitrary knowledge can be integrated with this mechanism, but in practice the knowledge representation language determines both the knowledge that can be integrated, and the costs of integration and induction. By instantiating KII with various set representations, algorithms can be generated at different trade-off points along these dimensions. One instantiation of KII, called RS-KII, is presented that can implement hybrid induction algorithms, depending on which knowledge it utilizes. RS-KII is demonstrated to implement AQ-11, as well as a hybrid algorithm that utilizes a domain theory and noisy examples. Other algorithms are also possible.
    Keywords: Documentation and Information Science
    Type: AD-A314831 , ISI/RS-96-438
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  • 3
    Publication Date: 2019-06-28
    Description: The use of knowledge in inductive learning is critical for improving the quality of the concept definitions generated, reducing the number of examples required in order to learn effective concept definitions, and reducing the computation needed to find good concept definitions. Relevant knowledge may come in many forms (such as examples, descriptions, advice, and constraints) and from many sources (such as books, teachers, databases, and scientific instruments). How to extract the relevant knowledge from this plethora of possibilities, and then to integrate it together so as to appropriately affect the induction process is perhaps the key issue at this point in inductive learning. Here the focus is on the integration part of this problem; that is, how induction algorithms can, and do, utilize a range of extracted knowledge. Preliminary work on a transformational framework for defining knowledge-intensive inductive algorithms out of relatively knowledge-free algorithms is described, as is a more tentative problems-space framework that attempts to cover all induction algorithms within a single general approach. These frameworks help to organize what is known about current knowledge-intensive induction algorithms, and to point towards new algorithms.
    Keywords: DOCUMENTATION AND INFORMATION SCIENCE
    Type: NASA. Johnson Space Center, The Seventh Annual Workshop on Space Operations Applications and Research (SOAR 1993), Volume 1; p 226-233
    Format: application/pdf
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  • 4
    Publication Date: 2019-07-12
    Description: The Uplink Summary Generator (ULSGEN) provides a convenient means of gathering together a set of uplink related files, parsing and analyzing these files, and producing a summary of their contents, which may then be electronically signed by one or more reviewers to verify the commands. Spacecraft operations personnel view this summary as a final sanity check before actual radiation of the uplink data.
    Keywords: Communications and Radar
    Type: NPO-48423 , NASA Tech Briefs, April 2013; 32
    Format: application/pdf
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  • 5
    Publication Date: 2019-07-12
    Description: Several artificial-intelligence search techniques have been tested as means of solving the swath segment selection problem (SSSP) -- a real-world problem that is not only of interest in its own right, but is also useful as a test bed for search techniques in general. In simplest terms, the SSSP is the problem of scheduling the observation times of an airborne or spaceborne synthetic-aperture radar (SAR) system to effect the maximum coverage of a specified area (denoted the target), given a schedule of downlinks (opportunities for radio transmission of SAR scan data to a ground station), given the limit on the quantity of SAR scan data that can be stored in an onboard memory between downlink opportunities, and given the limit on the achievable downlink data rate. The SSSP is NP complete (short for "nondeterministic polynomial time complete" -- characteristic of a class of intractable problems that can be solved only by use of computers capable of making guesses and then checking the guesses in polynomial time).
    Keywords: Documentation and Information Science
    Type: NPO-40454 , NASA Tech Briefs, September 2006; 49
    Format: application/pdf
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  • 6
    Publication Date: 2019-07-13
    Description: The Remote Agent (RA) is an Artificial Intelligence (AI) system which automates some of the tasks normally reserved for human mission operators and performs these tasks autonomously on-board the spacecraft. These tasks include activity generation, sequencing, spacecraft analysis, and failure recovery. The RA will be demonstrated as a flight experiment on Deep Space One (DSI), the first deep space mission of the NASA's New Millennium Program (NMP). As we moved from prototyping into actual flight code development and teamed with ground operators, we made several major extensions to the RA architecture to address the broader operational context in which PA would be used. These extensions support ground operators and the RA sharing a long-range mission profile with facilities for asynchronous ground updates; support ground operators monitoring and commanding the spacecraft at multiple levels of detail simultaneously; and enable ground operators to provide additional knowledge to the RA, such as parameter updates, model updates, and diagnostic information, without interfering with the activities of the RA or leaving the system in an inconsistent state. The resulting architecture supports incremental autonomy, in which a basic agent can be delivered early and then used in an increasingly autonomous manner over the lifetime of the mission. It also supports variable autonomy, as it enables ground operators to benefit from autonomy when L'@ey want it, but does not inhibit them from obtaining a detailed understanding and exercising tighter control when necessary. These issues are critical to the successful development and operation of autonomous spacecraft.
    Keywords: Cybernetics
    Type: RIACS-TR-98.08 , Autonomous Agents; Jan 01, 1998
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  • 7
    Publication Date: 2019-07-13
    Description: This viewgraph presentation reviews work on model checking, and specifically the SPIN model checker. The goal of this work is to retire a significant class of risks associated with the use of Artificial Intelligence (Al) Planners on Missions. This effort must provide tangible testing results to a mission using Al technology. It is hoped that the work should be possible to leverage the technique and tools throughout NASA
    Keywords: Cybernetics, Artificial Intelligence and Robotics
    Type: 4th Annual NASA OFfice of Safety and Mission Assurance Software Assurance Symposium; Jul 20, 2004 - Jul 22, 2004; Morgantown, WV; United States
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  • 8
    Publication Date: 2019-07-13
    Description: Jet Propulsion Laboratory uses multi-mission software produced by the Mission Planning and Sequencing (MPS) team to process, simulate, translate, and package the commands that are sent to a spacecraft. MPS works under the auspices of the Multi-Mission Ground Systems and Services (MGSS). This software consists of nineteen applications that are in maintenance. The MPS software is classified as either class B (mission critical) or class C (mission important). The scheduling of tasks is difficult because mission needs must be addressed prior to performing any other tasks and those needs often spring up unexpectedly. Keeping track of the tasks that everyone is working on is also difficult because each person is working on a different software component. Recently the group adopted the Scrum methodology for planning and scheduling tasks. Scrum is one of the newer methodologies typically used in agile development. In the Scrum development environment, teams pick their tasks that are to be completed within a sprint based on priority. The team specifies the sprint length usually a month or less. Scrum is typically used for new development of one application. In the Scrum methodology there is a scrum master who is a facilitator who tries to make sure that everything moves smoothly, a product owner who represents the user(s) of the software and the team. MPS is not the traditional environment for the Scrum methodology. MPS has many software applications in maintenance, team members who are working on disparate applications, many users, and is interruptible based on mission needs, issues and requirements. In order to use scrum, the methodology needed adaptation to MPS. Scrum was chosen because it is adaptable. This paper is about the development of the process for using scrum, a new development methodology, with a team that works on disparate interruptible tasks on multiple software applications.
    Keywords: Computer Programming and Software
    Type: International Conference on Space Operations; May 05, 2014 - May 09, 2014; Pasadena, CA; United States
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  • 9
    Publication Date: 2019-07-13
    Description: The RadarSAT Modified Antarctic Mapping Mission (MAMM) ran from September to November 2000. It consisted of over 2400 synthetic aperture radar (SAR) data takes over Antarctica that had to satisfy coverage and other scientific criteria while obeying tight resource and operational constraints. Developing these plans is a time and knowledge intensive effort. It required over a work-year to manually develop a comparable plan for AMM-1, the precursor mission to MAMM. This paper describes the automated mission planning system for MAMM, which dramatically reduced mission-planning costs to just a few workweeks, and enabled rapid generation of 'what-if' scenarios for evaluating mission-design trades. This latter capability informed several critical design decisions and was instrumental in accurately costing the mission.
    Keywords: Communications and Radar
    Type: Innovative Applications of Artificial Intelligence; Aug 07, 2001; Seattle, WA; United States
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  • 10
    Publication Date: 2019-07-13
    Description: This paper describes the automated mission planning system for MAMM, which dramatically reduced mission-planning costs to just a few workweeks, and enabled rapid generation of 'what-if' scenarios for evaluating mission-design trades. This latter capability informed several critical design decisions and was instrumental in accurately costing the mission.
    Type: 4th International Symposium on Reducing the Cost of Spacecraft Ground Systems and Operations; Apr 24, 2001; Laurel, MD; United States
    Format: text
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