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All sourcesNASA NTRSDOE OSTI
NASA NTRS2012

Climate Change Impacts on the Built Environment in the United States and Implications for Sustainability

As an integral part of the National Climate Assessment (NCA), technical assessment reports for 13 regions in the U.S. that describe the scientific rationale to support climate change impacts within the purview of these regions, and provide adaptation or mitigation measures in response to these impacts. These technical assessments focus on climate change impacts on sectors that are important environmental, biophysical, and social and economic aspects of sustainability within the U.S.: Climate change science, Ecosystems and biodiversity, Water resources, Human health, Energy supply and use, Water/energy/land use, Transportation, Urban/infrastructure/vulnerability, Agriculture, Impacts of climate change on tribal/indigenous and native lands and resources, Forestry, Land use/land cover change, Rural communities development, and Impacts on biogeochemical cycles, with implications for ecosystems and biodiversity. There is a critical and timely need for the development of mitigation and adaptation strategies in response to climate change by the policy and decision making communities, to insure resiliency and sustainability of the built environment in the future.

Quattrochi, Dale A.↗
NASA NTRS2015

Improving the Timeliness of Winter Wheat Production Forecast in the United States of America, Ukraine and China Using MODIS Data and NCAR Growing Degree Day Information

Wheat is themost important cereal crop traded on international markets and winter wheat constitutes approximately 80% of global wheat production. Thus, accurate and timely production forecasts are critical for making informed agricultural policies and investments, as well as increasing market efficiency and stability. Becker-Reshef et al. (2010) developed an empirical generalized model for forecasting winter wheat production. Their approach combined BRDF-corrected daily surface reflectance from Moderate resolution Imaging Spectroradiometer (MODIS) Climate Modeling Grid (CMG) with detailed official crop statistics and crop typemasks. It is based on the relationship between the Normalized Difference Vegetation Index (NDVI) at the peak of the growing season, percent wheat within the CMG pixel (area within the CMG pixel occupied by wheat), and the final yields. This method predicts the yield approximately one month to six weeks prior to harvest. In this study, we include Growing Degree Day (GDD) information extracted from NCEP/NCAR reanalysis data in order to improve the winter wheat production forecast by increasing the timeliness of the forecasts while conserving the accuracy of the original model. We apply this modified model to three major wheat-producing countries: the Unites States (US), Ukraine and China from 2001 to 2012. We show that a reliable forecast can be made between one month to a month and a half prior to the peak NDVI (meaning two months to two and a half months prior to harvest), while conserving an accuracy of 10% in the production forecast.

Production↗
NASA NTRS2016

Quantifying PM2.5-Meteorology Sensitivities in a Global Climate Model

Climate change can influence fine particulate matter concentrations (PM2.5) through changes in air pollution meteorology. Knowledge of the extent to which climate change can exacerbate or alleviate air pollution in the future is needed for robust climate and air pollution policy decision-making. To examine the influence of climate on PM2.5, we use the Geophysical Fluid Dynamics Laboratory Coupled Model version 3 (GFDL CM3), a fully-coupled chemistry-climate model, combined with future emissions and concentrations provided by the four Representative Concentration Pathways (RCPs). For each of the RCPs, we conduct future simulations in which emissions of aerosols and their precursors are held at 2005 levels while other climate forcing agents evolve in time, such that only climate (and thus meteorology) can influence PM2.5 surface concentrations. We find a small increase in global, annual mean PM2.5 of about 0.21 micro-g/cu m3 (5%) for RCP8.5, a scenario with maximum warming. Changes in global mean PM2.5 are at a maximum in the fall and are mainly controlled by sulfate followed by organic aerosol with minimal influence of black carbon. RCP2.6 is the only scenario that projects a decrease in global PM2.5 with future climate changes, albeit only by -0.06 micro-g/cu m (1.5%) by the end of the 21st century. Regional and local changes in PM2.5 are larger, reaching upwards of 2 micro-g/cu m for polluted (eastern China) and dusty (western Africa) locations on an annually averaged basis in RCP8.5. Using multiple linear regression, we find that future PM2.5 concentrations are most sensitive to local temperature, followed by surface wind and precipitation. PM2.5 concentrations are robustly positively associated with temperature, while negatively related with precipitation and wind speed. Present-day (2006-2015) modeled sensitivities of PM2.5 to meteorological variables are evaluated against observations and found to agree reasonably well with observed sensitivities (within 10e50% over the eastern United States for several variables), although the modeled PM2.5 is less sensitive to precipitation than in the observations due to weaker convective scavenging. We conclude that the hypothesized "climate penalty" of future increases in PM2.5 is relatively minor on a global scale compared to the influence of emissions on PM2.5 concentrations.

PM2.5↗
NASA NTRS2018

Enhancing Remote Sensing Based Yield Forecasting: Application to Winter Wheat in United States

Accurate and timely crop yield forecasts are critical for making informed agricultural policies and investments, as well as increasing market efficiency and stability. In Becker-Reshef et al. (2010) and Franch et al. (2015) we developed an empirical generalized model for forecasting winter wheat yield. In this study we present a new model based on the extrapolation of the pure wheat signal (100 percent of wheat within the pixel) from MODIS (Moderate-resolution Imaging Spectroradiometer) data at 1-kilometer resolution and using the Difference Vegetation Index (DVI). The model has been applied to monitor the national and state level yield of winter wheat in the United States from 2001 to 2016.

Agriculture↗
NASA NTRS2018

Remote Sensing Based Yield Monitoring: Application to Winter Wheat in United States and Ukraine

Accurate and timely crop yield forecasts are critical for making informed agricultural policies and investments, as well as increasing market efficiency and stability. Earth observation data from space can contribute to agricultural monitoring, including crop yield assessment and forecasting. In this study, we present a new crop yield model based on the Difference Vegetation Index (DVI) extracted from Moderate Resolution Imaging Spectroradiometer (MODIS) data at 1 km resolution and the un-mixing of DVI at coarse resolution to a pure wheat signal (100 percent of wheat within the pixel). The model was applied to estimate the national and subnational winter wheat yield in the United States and Ukraine from 2001 to 2017. The model at the subnational level shows very good performance for both countries with a coefficient of determination higher than 0.7 and a root mean square error (RMSE) of lower than 0.6 t/ha (tonnes per hectare) (15-18 percent). At the national level for the United States (US) and Ukraine the model provides a strong coefficient of determination of 0.81 and 0.86, respectively, which demonstrates good performance at this scale. The model was also able to capture low winter wheat yields during years with extreme weather events, for example 2002 in US and 2003 in Ukraine. The RMSE of the model for the US at the national scale is 0.11 t/ha (3.7 percent) while for Ukraine it is 0.27 t/ha (8.4 percent).

DVI↗
NASA NTRS2018

Remote Sensing Based Yield Monitoring: Application to Winter Wheat in United States and Ukraine

Accurate and timely crop yield forecasts are critical for making informed agricultural policies and investments, as well as increasing market efficiency and stability. Earth observation data from space can contribute to agricultural monitoring, including crop yield assessment and forecasting. In this study, we present a new crop yield model based on the Difference Vegetation Index (DVI) extracted from Moderate Resolution Imaging Spectroradiometer (MODIS) data at 1 km resolution and the un-mixing of DVI at coarse resolution to a pure wheat signal (100% of wheat within the pixel). The model was applied to estimate the national and subnational winter wheat yield in the United States and Ukraine from 2001 to 2017. The model at the subnational level shows very good performance for both countries with a coefficient of determination higher than 0.7 and a root mean square error (RMSE) of lower than 0.6 t/ha (15–18%). At the national level for the United States (US) and Ukraine the model provides a strong coefficient of determination of 0.81 and 0.86, respectively, which demonstrates good performance at this scale. The model was also able to capture low winter wheat yields during years with extreme weather events, for example 2002 in US and 2003 in Ukraine. The RMSE of the model for the US at the national scale is 0.11 t/ha (3.7%) while for Ukraine it is 0.27 t/ha (8.4%).

remote sensing↗
NASA NTRS2021

Peru Health & Air Quality: Land Use Change in the Rapidly Developing Peruvian Amazon and Implications on Zoonotic Disease Incidence

In the Madre de Dios region of the Peruvian Amazon, forests are being cleared for mining, timber harvesting, road construction, and hydroelectric dam development. These rapid land use changes are increasing human presence in previously sparsely populated areas, disrupting ecosystems and increasing the proximity of human settlement to zoonotic disease vectors. Dengue fever and leishmaniasis are two neglected tropical diseases which are prevalent in Madre de Dios and have been associated with urbanization and road construction. In partnership with the Peruvian Ministries of Health (MINSA) and the Environment (MINAM) and other in-country collaborators, our team examined Land Use Land Cover (LULC) correlations with reported dengue and leishmaniasis incidence in the Madre de Dios region to help partners understand the spatial relationship between land use change and zoonotic disease incidence. We created a LULC classification script using Google Earth Engine with Landsat 5 Thematic Mapper and Landsat 8 Operational Land Imager imagery to classify land cover in 2010, 2015, and 2020 and evaluate changes over this time period. We then used the quantified results of the LULC assessment in conjunction with reported disease cases to evaluate correlations between disease incidence and key land cover changes across Madre de Dios’s 11 districts. In the second term, the team will use these products to develop more detailed disease incidence risk maps and models. High risk areas will then be classified, using PeruSat-1 that allow for even higher resolution mapping at less than 3 meters. These products will allow the partners to understand hotspots of land cover change in Peru and the relationship with outbreaks to inform public health decision making and environmental policy.

Elizabeth Stapleton↗
NASA NTRS2021

Integrating Inland and Coastal Water Quality Data for Actionable Knowledge

Water quality measures for inland and coastal waters are available as discrete samples from professional and volunteer water quality monitoring programs and higher-frequency, near-continuous data from automated in situ sensors. Water quality parameters also are estimated from model outputs and remote sensing. The integration of these data, via data assimilation, can result in a more holistic characterization of these highly dynamic ecosystems, and consequently improve water resource management. It is becoming common to see combinations of these data applied to answer relevant scientific questions. Yet, methods for scaling water quality data across regions and beyond, to provide actionable knowledge for stakeholders, have emerged only recently, particularly with the availability of satellite data now providing global coverage at high spatial resolution. In this paper, data sources and existing data integration frameworks are reviewed to give an overview of the present status and identify the gaps in existing frameworks. We propose an integration framework to provide information to user communities through the the Group on Earth Observations (GEO) AquaWatch Initiative. This aims to develop and build the global capacity and utility of water quality data, products, and information to support equitable and inclusive access for water resource management, policy and decision making.

Ghada Y.H. El Serafy↗
NASA NTRSDate not supplied

Diversity and Inclusion in Spacecraft Science Teams: What Do We Know and What Can We Do About It?

Introduction: Not only does the planetary science community lack diversity [1-3], the subset of the community that participates on spacecraft science team is even less diverse than the community as a whole [1, 4]. Results of 2020 Workforce Survey: Previous studies of the diversity of members of spacecraft science teams made incorrect assumptions about the nature of the data before collecting the data. Those analyses assumed a binary gender and ignored the existence of planetary scientists who are neither men nor women [4-6]. We present here results where demographic data was collected without assumptions; each individual surveyed supplied their own answers to demographic questions. The April 2020 survey of Planetary Scientists, which was conducted by the Statistical Research Center of the American Institute of Physics (AIP) and funded by the American Astronomical Society (AAS)’s Division of Planetary Science (DPS) asked participants their gender with 4 possible responses: Woman, Man, Another identify (please specify if you wish), and Prefer not to answer. 32% of respondents chose Woman, 67% chose Man and 1% chose Another gender identity [1]. The survey also asked demographic questions on race, ethnicity, LGBTQ+ identity, and disability. For a full list of questions, see https://dps.aas.org/sites/dps.aas.org/files/reports/2020/survey2020_questionnaire.pdf. In addition to demographic questions, the 2020 Workforce survey asked how many times respondents had been involved in Mission proposals as a Principal Investigator (PI) and, separately, as a Co-Investigator (CoI) [1]. Answers to questions about mission involvement were correlated with answers to demographic questions and the results show that members of historically underrepresented groups (non-white scientists, women, members of the LGBTQ+ community, and disabled scientists) were less likely to be involved in spacecraft mission proposals than were members of historically overrepresented groups [1]. The figures below show the correlated responses for four different axes of underrepresentation [1]. Note that while the figure on gender shows only Women and Men (due to the small percentage of folks answering “Another gender”), non-binary respondents are included in the LGBTQ+ community figure. Conclusion: Being part of a spacecraft science team is a goal for many planetary scientists. With it comes brand new data, more stable funding, and a sense of awe and exploration. It can lead to a cascade of opportunities from conference and public presentations, to membership in subsequent mission teams, and prestige in the community [4]. As a result, participation in spacecraft teams can be used a measure of success within the field. From the survey results, we see that members of historically excluded groups, even after they have overcome barriers to participating in the field, are still experiencing barriers to success within the field itself. Why?: The diminishing percentage of members of underrepresented groups as a career progresses has been referred to as a “leaky pipeline”. However, this fails to adequately capture the experiences of the members of these underrepresented groups as it implies a passive process. In order to capture the active processes (bias, discrimination, harassment, and other exclusionary behaviors) that contribute to low retention in the workforce, the term “Hostile Obstacle Course” is more useful [7,8]. It is these processes that need to be addressed in order to retain valued members of our community. Moving Forward: In order to broaden participation in planetary science, particularly mission science teams, we need to address conditions that create hostile workplace climates. What can mission teams and other groups do to address these conditions? First, each group/team needs to evaluate their own members to determine what specific barriers exist in their own interactions. One tool to accomplish this would be an anonymous survey designed to understand how team members feel about working within the group. Working with professionals who know how to create and analyze such surveys (often called “climate surveys” when applied to University students, for example) would ensure that the survey meets its goals and does not make assumptions that counter the meaningfulness of the results. Such professionals in EDIA (Equity, Diversity, Inclusion, and Accessibility) and workplace culture can make suggestions for policy changes that would eliminate hostile workplace conditions. Policy changes that are often suggested include instituting professional EDIA training for the team and/or for team leadership, instituting and following a code of conduct [9], including more interactive group activities in group meetings, etc. Training and information on EDIA is available for all members of the planetary science community. The first place to look would be in your University or Institution’s EDIA or human resources offices. Bystander Intervention is often offered as part of other meetings [10]. A newer offering is a Workshop on EDIA for Leaders in Planetary Science led by Julie Rathbun (first author of this abstract) and JA Grier (https://edialps.psi.edu/). This 3-day workshop gives participants the tools they need to enact positive change in their personal and professional spheres. The first workshop was help in November 2022 and another workshop will take place in the late spring 2023 with exact dates to be announced soon.

J. A. Rathbun↗
NASA NTRS2024

Quantifying Food Security and Mitigation Risks Consequential to Climate Change Impacts on Crop Yields

Climate change is expected to impact crop yields globally, with some regions benefiting from favorable conditions and CO2 fertilization, while others face adverse effects from altered precipitation and higher temperatures. Changes in crop yields can destabilize the global food system and pose challenges to food security. Moreover, crop production is crucial, as biofuels are becoming increasingly important contributors to climate change mitigation measures aimed at limiting global warming. This study uses the Integrated Model to Assess the Global Environment integrated assessment model framework to analyze different indicators related to food security and climate change mitigation under varying climate change impacts on crop yields. Twelve spatially explicit crop productivity projections were taken from the full archive of the Global Gridded Crop Model Intercomparison of 120 climate-crop model combinations, forced by CMIP6-based climate scenarios. The selection includes two average-performing climate-crop model combinations, two pessimistic combinations that perform one standard deviation below the mean, and two optimistic model combinations that perform one standard deviation above the mean. To single out the effect of climate change on productivity changes, we drew samples from two representative concentration pathways (RCP2.6 and RCP8.5). These productivity projections were applied within an otherwise uniform scenario (SSP2) and analyzed for their effect on total calorie demand, crop prices, and number of people at risk of undernourishment to quantify food security. Risks to climate change mitigation targets were explored by modeling the total bioenergy supply, emissions, and global mean temperature. The results revealed significant differences in the risk of food security and mitigation potential between different regions and climate change scenarios. Across scenarios, the crop area extent can vary up to 2 million km2 due to changing crop yields. The projected change in global hunger ranges from 60 to 160 million undernourished people, indicating uncertainty between climate and crop model combinations. Low-income regions are especially impacted because of their high sensitivity to changes in food prices. Global climate change mitigation ambitions can also deviate by the latter part of the 21st century, as changes in yields will impact biofuel production as well as agriculture, forestry and other land use emissions. The quantitative insights generated by this study highlight the need for global policy efforts to make the agricultural system more adaptive to climate change to handle potential negative impacts.

crop yields↗
NASA NTRS1984

The ideology of science during the Nixon years: 1970-1976

This paper examines the expert testimony given before the U.S. Congress during the legislative history of the National Science and Technology Policy Organization and Priorities Act of 1976, examining in particular the scientific witnesses' appeals for enlarged government funding for basic research and greater influence in the making of federal science policy. The author finds that in the process of arguing for increased support and influence, spokesmen for the nation's science establishment articulated an ideology of science which not only proclaimed the authority of scientific values over other forms of cognition but sought to advance the authority of scientists over the identification and resolution of societal and political issues. In so doing they challenged the viability of political values essential to the Anglo-American democratic-republican heritage. The paper thus documents not only cultural 'elitism,' which is not necessarily incompatible with democratic politics, but an antidemocratic ideology as well.

Fries, Sylvia Doughty↗
NASA NTRS1998

Scale in Remote Sensing and GIS: An Advancement in Methods Towards a Science of Scale

The term "scale", both in space and time, is central to remote sensing and Geographic Information Systems (GIS). The emergence and widespread use of GIS technologies, including remote sensing, has generated significant interest in addressing scale as a generic topic, and in the development and implementation of techniques for dealing explicitly with the vicissitudes of scale as a multidisciplinary issue. As science becomes more complex and utilizes databases that are capable of performing complex space-time data analyses, it becomes paramount that we develop the tools and techniques needed to operate at multiple scales, to work with data whose scales are not necessarily ideal, and to produce results that can be aggregated or disaggregated ways that suit the decision-making process. Contemporary science is constantly coping with compromises, and the data available for a particular study rarely fit perfectly with the scales at which the processes being investigated operate, or the scales that policy-makers require to make sound, rational decisions. This presentation discusses some of the problems associated with scale as related to remote sensing and GIS, and describes some of the questions that need to be addressed in approaching the development of a multidisciplinary "science of scale". Techniques for dealing with multiple scaled data that have been developed or explored recently are described as a means for recognizing scale as a generic issue, along with associated theory and tools that can be of simultaneous value to a large number of disciplines. These can be used to seek answers to a host of interrelated questions in the interest of providing a formal structure for the management and manipulation of scale and its universality as a key concept from a multidisciplinary perspective.

Quattrochi, D. A.↗
NASA NTRS1998

Scale in Remote Sensing and GIS: An Advancement in Methods Towards a Science of Scale

The term "scale", both in space and time, is central to remote sensing and geographic information systems (GIS). The emergence and widespread use of GIS technologies, including remote sensing, has generated significant interest in addressing scale as a generic topic, and in the development and implementation of techniques for dealing explicitly with the vicissitudes of scale as a multidisciplinary issue. As science becomes more complex and utilizes databases that are capable of performing complex space-time data analyses, it becomes paramount that we develop the tools and techniques needed to operate at multiple scales, to work with data whose scales are not necessarily ideal, and to produce results that can be aggregated or disaggregated in ways that suit the decision-making process. Contemporary science is constantly coping with compromises, and the data available for a particular study rarely fit perfectly with the scales at which the processes being investigated operate, or the scales that policy-makers require to make sound, rational decisions. This presentation discusses some of the problems associated with scale as related to remote sensing and GIS, and describes some of the questions that need to be addressed in approaching the development of a multidisciplinary "science of scale". Techniques for dealing with multiple scaled data that have been developed or explored recently are described as a means for recognizing scale as a generic issue, along with associated theory and tools that can be of simultaneous value to a large number of disciplines. These can be used to seek answers to a host of interrelated questions in the interest of providing a formal structure for the management and manipulation of scale and its universality as a key concept from a multidisciplinary perspective.

Quattrochi, Dale A.↗
NASA NTRS2010

Achieving a Risk-Informed Decision-Making Environment at NASA: The Emphasis of NASA's Risk Management Policy

This slide presentation reviews the evolution of risk management (RM) at NASA. The aim of the RM approach at NASA is to promote an approach that is heuristic, proactive, and coherent across all of NASA. Risk Informed Decision Making (RIDM) is a decision making process that uses a diverse set of performance measures along with other considerations within a deliberative process to inform decision making. RIDM is invoked for key decisions such as architecture and design decisions, make-buy decisions, and budget reallocation. The RIDM process and how it relates to the continuous Risk Management (CRM) process is reviewed.

Dezfuli, Homayoon↗
NASA NTRS2014

Providing Global Change Information for Decision-Making: Capturing and Presenting Provenance

Global change information demands access to data sources and well-documented provenance to provide evidence needed to build confidence in scientific conclusions and, in specific applications, to ensure the information's suitability for use in decision-making. A new generation of Web technology, the Semantic Web, provides tools for that purpose. The topic of global change covers changes in the global environment (including alterations in climate, land productivity, oceans or other water resources, atmospheric composition and or chemistry, and ecological systems) that may alter the capacity of the Earth to sustain life and support human systems. Data and findings associated with global change research are of great public, government, and academic concern and are used in policy and decision-making, which makes the provenance of global change information especially important. In addition, since different types of decisions benefit from different types of information, understanding how to capture and present the provenance of global change information is becoming more of an imperative in adaptive planning.

provenance tracking↗
NASA NTRSDate not supplied

TPSAS-NF1676L-32124-DND

The NASA DEVELOP National Program seeks to simultaneously build capacity to use Earth observations in early career and transitioning professionals while building capacity with institutional partners to apply Earth observations in conducting operations, making decisions, or informing policy. This is done through 10-week feasibility projects, conducted by the DEVELOP teams in collaboration with decision makers. The program carries out 60-80 projects each year, engaging with over 120 partners from local, state, and federal governments to academic institutions and NGOs. To best understand project partner needs, projects begin with a thorough proposal development process in which partners share their current practices, needs, and capabilities. Throughout the project life cycle, the DEVELOP teams engage with the partners to ensure communication, feedback, and understanding. DEVELOP’s model of conducting rapid feasibility projects is an effective way to show decision maker show they can use NASA Earth science data in new ways to help them make informed decisions. Because the projects are conducted by a DEVELOP team in collaboration with end users, the partners are introduced to new data products and methodologies that they otherwise might not be able to explore within their own resource constraints. This presentation will discuss project examples, success stories, and best practices for engaging with decision makers on applied science projects.

Amanda Clayton↗
NASA NTRS1990

Data policy and availability supporting global change research, development, and decision-making: An information perspective

An explosion of information has created a crisis for today's information age. It has to be determined how to use the best available information sources, tools, and technology. To do this it is necessary to have leadership at the interagency level to promote a coherent information policy. It is also important to find ways to educate the users of information regarding the tools available to them. Advances in technology resulted in efforts to shift from Disciplinary and Mission-oriented Systems to Decision Support Systems and Personalized Information Systems. One such effort is being made by the Interagency Working Group on Data Management for Global Change (IAWGDMGC). Five federal agencies - the Department of Commerce (DOC), Department of Energy (DOE), National Aeronautics and Space Administration (NASA), National Library of Medicine (NLM), and Department of Defense (DOD) - have an on-going cooperative information management group, CENDI (Commerce, Energy, NASA, NLM, and Defense Information), that is meeting the challenge of coordinating and integrating their information management systems. Although it is beginning to be technically feasible to have a system with text, bibliographic, and numeric data online for the user to manipulate at the user's own workstation, it will require national recognition that the resource investment in such a system is worthwhile, in order to promote its full development. It also requires close cooperation between the producers and users of the information - that is, the research and policy community, and the information community. National resources need to be mobilized in a coordinated manner to move people into the next generation of information support systems.

Carroll, Bonnie C.↗
NASA NTRS1977

Controlled flexibility in technical editing - The levels-of-edit concept at JPL

The levels-of-edit concept, which can be used to specify the amount of editorial effort involved in the preparation of a manuscript for publication, is discussed. Nine types of editing are identified and described. These include coordination edit (preparing estimates, gathering cost data, monitoring production processes), policy edit, integrity edit (making sure that parts of a publication match in a physical or numerical sense), screening edit (ensuring that the quality of camera-ready copy is sufficient for external publication), copy clarification edit, format edit, mechanical style edit, language edit, and substantive edit (reviewing the manuscript for content coherence, emphasis, subordination and parallelism). These functions are grouped into five levels of edit. An edit-level number is assigned to each manuscript, providing a quantitative and qualitative indicator of the editing to be done which is clearly understood by authors, managers, and editors alike. In addition, clear boundaries are drawn between normal and extraordinary editing tasks. Individual organizations will group various edits in different ways to reflect their needs and priorities; the essential element of the system is unambiguous definition and coding of the types and amount of work to be done.

Buehler, M. F.↗