EcoService Models Library (ESML)
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Compare EMs
Which comparison is best for me?EM Variables by Variable Role
One quick way to compare ecological models (EMs) is by comparing their variables. Predictor variables show what kinds of influences a model is able to account for, and what kinds of data it requires. Response variables show what information a model is capable of estimating.
This first comparison shows the names (and units) of each EM’s variables, side-by-side, sorted by variable role. Variable roles in ESML are as follows:
- Predictor Variables
- Time- or Space-Varying Variables
- Constants and Parameters
- Intermediate (Computed) Variables
- Response Variables
- Computed Response Variables
- Measured Response Variables
EM Variables by Category
A second way to use variables to compare EMs is by focusing on the kind of information each variable represents. The top-level categories in the ESML Variable Classification Hierarchy are as follows:
- Policy Regarding Use or Management of Ecosystem Resources
- Land Surface (or Water Body Bed) Cover, Use or Substrate
- Human Demographic Data
- Human-Produced Stressor or Enhancer of Ecosystem Goods and Services Production
- Ecosystem Attributes and Potential Supply of Ecosystem Goods and Services
- Non-monetary Indicators of Human Demand, Use or Benefit of Ecosystem Goods and Services
- Monetary Values
Besides understanding model similarities, sorting the variables for each EM by these 7 categories makes it easier to see if the compared models can be linked using similar variables. For example, if one model estimates an ecosystem attribute (in Category 5), such as water clarity, as a response variable, and a second model uses a similar attribute (also in Category 5) as a predictor of recreational use, the two models can potentially be used in tandem. This comparison makes it easier to spot potential model linkages.
All EM Descriptors
This selection allows a more detailed comparison of EMs by model characteristics other than their variables. The 50-or-so EM descriptors for each model are presented, side-by-side, in the following categories:
- EM Identity and Description
- EM Modeling Approach
- EM Locations, Environments, Ecology
- EM Ecosystem Goods and Services (EGS) potentially modeled, by classification system
EM Descriptors by Modeling Concepts
This feature guides the user through the use of the following seven concepts for comparing and selecting EMs:
- Conceptual Model
- Modeling Objective
- Modeling Context
- Potential for Model Linkage
- Feasibility of Model Use
- Model Certainty
- Model Structural Information
Though presented separately, these concepts are interdependent, and information presented under one concept may have relevance to other concepts as well.
EM Identity and Description
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EM ID
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EM-84 |
EM-127 |
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EM Short Name
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ACRU, South Africa | Annual profit - carbon plantings, South Australia |
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EM Full Name
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ACRU (Agricultural Catchments Research Unit), South Africa | Annual profit from carbon plantings, South Australia |
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EM Source or Collection
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None | None |
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EM Source Document ID
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271 | 243 |
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Document Author
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Egoh, B., Reyers, B., Rouget, M., Richardson, D.M., Le Maitre, D.C., and van Jaarsveld, A.S. | Crossman, N. D., Bryan, B. A., and Summers, D. M. |
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Document Year
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2008 | 2011 |
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Document Title
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Mapping ecosystem services for planning and management | Carbon payments and low-cost conservation |
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Document Status
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Peer reviewed and published | Peer reviewed and published |
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Comments on Status
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Published journal manuscript | Published journal manuscript |
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EM ID
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EM-84 |
EM-127 |
| Not applicable | Not applicable | |
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Contact Name
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Roland E Schulze | Neville D. Crossman |
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Contact Address
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School of Bioresources Engineering and Environmental Hydrology, University of Natal, South Africa | CSIRO Ecosystem Sciences, PMB 2, Glen Osmond, South Australia, 5064, Australia |
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Contact Email
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schulzeR@nu.ac.za | neville.crossman@csiro.au |
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EM ID
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EM-84 |
EM-127 |
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Summary Description
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AUTHOR'S DESCRIPTION (Doc ID 272): "ACRU is a daily timestep, physical conceptual and multipurpose model structured to simulate impacts of land cover/ use change. The model can output, inter alia, components of runoff, irrigation supply and demand, reservoir water budgets as well as sediment and crop yields." AUTHOR'S DESCRIPTION (Doc ID 271): "We define the range of ecosystem services as areas of meaningful supply, similar to a species’ range or area of occupancy. The term ‘‘hotspots’’ was proposed by Norman Myers in the 1980s and refers to areas of high species richness, endemism and/or threat and has been widely used to prioritise areas for biodiversity conservation. Similarly, this study suggests that hotspots for ecosystem services are areas of critical management importance for the service. Here the term ecosystem service hotspot is used to refer to areas which provide large proportions of a particular service, and do not include measures of threat or endemism…The total benefit to people of water supply is a function of both the quantity and quality with the ecosystem playing a key role in the latter. However, due to the lack of suitable national scale data on water quality for quantifying the service, runoff was used as an estimate of the benefit where runoff is the total water yield from a watershed including surface and subsurface flow. This assumes that runoff is positively correlated with quality, which is the case in South Africa (Allanson et al., 1990)…In South Africa, water resources are mapped in water management areas called catchments (vs. watersheds) where a catchment is defined as the area of land that is drained by a single river system, including its tributaries (DWAF, 2004). There are 1946 quaternary (4th order) catchments in South Africa, the smallest is 4800 ha and the average size is 65,000 ha. Schulze (1997) modelled annual runoff for each quaternary catchment. During modelling of runoff, he used rainfall data collected over a period of more than 30 years, as well as data on other climatic factors, soil characteristics and grassland as the land cover. In this study, median annual simulated runoff was used as a measure of surface water supply. The volume of runoff per quaternary catchment was calculated for surface water supply. The range (areas with runoff of 30 million m^3 or more) and hotspots (areas with runoff of 70 million m^3 or more) were defined using a combination of statistics and expert inputs due to a lack of published thresholds in the literature." | ABSTRACT: "A price on carbon is expected to generate demand for carbon offset schemes. This demand could drive investment in tree-based monocultures that provide higher carbon yields than diverse plantings of native tree and shrub species, which sequester less carbon but provide greater variation in vegetation structure and composition. Economic instruments such as species conservation banking, the creation and trading of credits that represent biological-diversity values on private land, could close the financial gap between monocultures and more diverse plantings by providing payments to individuals who plant diverse species in locations that contribute to conservation and restoration goals. We studied a highly modified agricultural system in southern Australia that is typical of many temperate agriculture zones globally (i.e., has a high proportion of endangered species, high levels of habitat fragmentation, and presence of non-native species). We quantified the economic returns...from carbon plantings (monoculture and mixed tree and shrubs) under six carbon-price scenarios." AUTHOR'S DESCRIPTION: "The economic returns of carbon plantings are highly variable and depend primarily on carbon yield and price and opportunity costs (Newell & Stavins 2000; Richards & Stokes 2004; Torres et al. 2010)...The spatial variation in carbon yield and costs, including establishment, maintenance, transaction, and opportunity costs, means that the net economic returns of carbon plantings are also likely to vary spatially." |
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Specific Policy or Decision Context Cited
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None identified | None identified |
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Biophysical Context
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Semi-arid environment. Rainfall varies geographically from less than 50 to about 3000 mm per year (annual mean 450 mm). Soils are mostly very shallow with limited irrigation potential. | Mix of remnant native vegetation and agricultural land. Remnant vegetation is in 20 large (>10,000 ha) contiguous fragments where rainfall is low. Acacia spp. and Eucalyptus spp. are the dominant tree species in the remnant vegetation, and major native vegetation types are open forests, woodlands, and open woodlands. Dominant agricultural uses are annual crops, annual legumes, and grazing of sheep and cows. The climate is Mediterranean with average annual rainfall ranging from 250 mm to 1000 mm. |
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EM Scenario Drivers
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No scenarios presented | Carbon prices at $10/t CO2^-e, $15/t CO2^-e, $20/t CO2^-e, $25/t CO2^-e, $30/t CO2^-e, and $40/t CO2^-e |
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EM ID
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EM-84 |
EM-127 |
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Method Only, Application of Method or Model Run
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Method + Application |
Method + Application (multiple runs exist) View EM Runs ?Comment:Runs are differentiated based on the the expected annual profit from two types of carbon plantings: 1) Tree-based monocultures (i.e., monoculture carbon planting) and 2) Diverse plantings of native tree and shrub species (i.e., ecological carbon planting) |
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New or Pre-existing EM?
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Application of existing model | New or revised model |
Related EMs (for example, other versions or derivations of this EM) described in ESML
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EM ID
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EM-84 |
EM-127 |
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Document ID for related EM
em.detail.relatedEmDocumentIdHelp
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Doc-272 ?Comment:Doc ID 272 was also used as a source document for this EM |
Doc-245 | Doc-246 | Doc-247 | Doc-243 |
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EM ID for related EM
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None | EM-128 | EM-141 |
EM Modeling Approach
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EM ID
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EM-84 |
EM-127 |
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EM Temporal Extent
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1950-1993 | 2009-2050 |
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EM Time Dependence
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time-dependent | time-dependent |
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EM Time Reference (Future/Past)
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future time | future time |
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EM Time Continuity
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discrete | discrete |
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EM Temporal Grain Size Value
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1 | 1 |
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EM Temporal Grain Size Unit
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Day | Year |
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EM ID
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EM-84 |
EM-127 |
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Bounding Type
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Geopolitical | Physiographic or Ecological |
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Spatial Extent Name
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South Africa | Agricultural districts of the state of South Australia |
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Spatial Extent Area (Magnitude)
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>1,000,000 km^2 | 100,000-1,000,000 km^2 |
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EM ID
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EM-84 |
EM-127 |
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EM Spatial Distribution
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spatially distributed (in at least some cases) | spatially distributed (in at least some cases) |
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Spatial Grain Type
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other (specify), for irregular (e.g., stream reach, lake basin) | area, for pixel or radial feature |
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Spatial Grain Size
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Distributed by catchments with average size of 65,000 ha | 1 ha x 1 ha |
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EM ID
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EM-84 |
EM-127 |
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EM Computational Approach
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Numeric | Analytic |
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EM Determinism
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deterministic | deterministic |
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Statistical Estimation of EM
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EM ID
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EM-84 |
EM-127 |
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Model Calibration Reported?
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No | No |
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Model Goodness of Fit Reported?
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No | No |
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Goodness of Fit (metric| value | unit)
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None | None |
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Model Operational Validation Reported?
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No | No |
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Model Uncertainty Analysis Reported?
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No | No |
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Model Sensitivity Analysis Reported?
em.detail.sensAnalysisHelp
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No | No |
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Model Sensitivity Analysis Include Interactions?
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Not applicable | Not applicable |
EM Locations, Environments, Ecology
Terrestrial location (Classification hierarchy: Continent > Country > U.S. State [United States only])
| EM-84 |
EM-127 |
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Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
| EM-84 |
EM-127 |
| None | None |
Centroid Lat/Long (Decimal Degree)
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EM ID
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EM-84 |
EM-127 |
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Centroid Latitude
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-30 | -34.9 |
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Centroid Longitude
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25 | 138.7 |
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Centroid Datum
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WGS84 | WGS84 |
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Centroid Coordinates Status
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Estimated | Estimated |
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EM ID
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EM-84 |
EM-127 |
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EM Environmental Sub-Class
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Rivers and Streams | Ground Water | Terrestrial Environment (sub-classes not fully specified) | Agroecosystems |
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Specific Environment Type
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Not reported | Agricultural land for annual crops, annual legumes, and grazing of sheep and cows |
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EM Ecological Scale
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Ecological scale is coarser than that of the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class |
Scale of differentiation of organisms modeled
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EM ID
em.detail.idHelp
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EM-84 |
EM-127 |
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EM Organismal Scale
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Not applicable | Guild or Assemblage |
Taxonomic level and name of organisms or groups identified
| EM-84 |
EM-127 |
| None Available |
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EnviroAtlas URL
| EM-84 |
EM-127 |
| Average Annual Precipitation | Carbon Storage by Tree Biomass |
EM Ecosystem Goods and Services (EGS) potentially modeled, by classification system
CICES v 4.3 - Common International Classification of Ecosystem Services (Section > Division > Group > Class)
| EM-84 |
EM-127 |
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<a target="_blank" rel="noopener noreferrer" href="https://www.epa.gov/eco-research/national-ecosystem-services-classification-system-nescs-plus">National Ecosystem Services Classification System (NESCS) Plus</a>
(Environmental Subclass > Ecological End-Product (EEP) > EEP Subclass > EEP Modifier)
| EM-84 |
EM-127 |
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None |
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