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
em.detail.idHelp
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EM-449 |
EM-719 |
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EM Short Name
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Decrease in erosion (shoreline), St. Croix, USVI | Seed mix for native plant establishment, IA, USA |
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EM Full Name
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Decrease in erosion (shoreline) by reef, St. Croix, USVI | Cost-effective seed mix design for native plant establishment, Iowa, USA |
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EM Source or Collection
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US EPA | None |
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EM Source Document ID
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335 | 394 |
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Document Author
em.detail.documentAuthorHelp
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Yee, S. H., Dittmar, J. A., and L. M. Oliver | Meissen, J. |
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Document Year
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2014 | 2018 |
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Document Title
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Comparison of methods for quantifying reef ecosystem services: A case study mapping services for St. Croix, USVI | Cost-effective seed mix design and first-year management |
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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 report |
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EM ID
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EM-449 |
EM-719 |
| Not applicable | Not applicable | |
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Contact Name
em.detail.contactNameHelp
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Susan H. Yee | Justin Meissen |
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Contact Address
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US EPA, Office of Research and Development, NHEERL, Gulf Ecology Division, Gulf Breeze, FL 32561, USA | Tallgrass Prairie Center, University of Northern Iowa |
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Contact Email
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yee.susan@epa.gov | Not reported |
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EM ID
em.detail.idHelp
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EM-449 |
EM-719 |
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Summary Description
em.detail.summaryDescriptionHelp
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ABSTRACT: "...We investigated and compared a number of existing methods for quantifying ecological integrity, shoreline protection, recreational opportunities, fisheries production, and the potential for natural products discovery from reefs. Methods were applied to mapping potential ecosystem services production around St. Croix, U.S. Virgin Islands. Overall, we found that a number of different methods produced similar predictions." AUTHOR'S DESCRIPTION: "A number of methods have been developed for linking biophysical attributes of reef condition, such as reef structural complexity, fish biomass, or species richness, to provisioning of ecosystem goods and services (Principe et al., 2012). We investigated the feasibility of using existing methods and data for mapping production of reef ecosystem goods and services. We applied these methods toward mapping potential ecosystem goods and services production in St. Croix, U.S. Virgin Islands (USVI)...For each of the five categories of ecosystem services, we chose a suite of models and indices for estimating potential production based on relative ease of implementation, consisting of well-defined parameters, and likely availability of input data, to maximize potential for transferability to other locations. For each method, we assembled the necessary reef condition and environmental data as spatial data layers for St. Croix (Table1). The coastal zone surrounding St. Croix was divided into 10x10 m grid cells, and production functions were applied to quantify ecosystem services provisioning in each grid cell...Shoreline protection as an ecosystem service has been defined in a number of ways including protection from shoreline erosion...and can thus be estimated as % Decrease in erosion due to reef = 1 - (Ho/H)^2.5 where Ho is the attenuated wave height due to the presence of the reef and H is wave height in the absence of the reef." | AUTHOR'S DESCRIPTION: "Restoring ecosystem services at scale requires executing conservation programs in a way that is resource and cost efficient as well as ecologically effective…Seed mix design is one of the largest determinants of project cost and ecological outcomes for prairie reconstructions. In particular, grass-to-forb seeding ratio affects cost since forb seed can be much more expensive relative to grass species (Prairie Moon Nursery 2012). Even for seed mixes with the same overall seeding rates, a mix with a low grass-to-forb seeding ratio is considerably more expensive than one with a high grass-to-forb ratio. Seeding rates for different plant functional groups that are too high or low may also adversely affect ecological outcomes…First-year management may also play a role in cost-effective prairie reconstruction. Post-agricultural sites where restoration typically occurs are often quickly dominated by fast-growing annual weeds by the time sown prairie seeds begin germinating (Smith et al. 2010)… Williams and others (2007) showed that prairie seedlings sown into established warm-season grasses were reliant on high light conditions created by frequently mowing tall vegetation in order to survive in subsequent years…Our objective was to compare native plant establishment and cost effectiveness with and without first-year mowing for three different seed mixes that differed in grass to forb ratio and soil type customization. With knowledge of plant establishment, cost effectiveness, and mowing management outcomes, conservation practitioners will be better equipped to restore prairie efficiently and successfully." |
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Specific Policy or Decision Context Cited
em.detail.policyDecisionContextHelp
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None identified | Seed mix design and management practices for native plant restoration |
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Biophysical Context
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No additional description provided | The soils underlying the study site are primarily poorly drained Clyde clay loams, with a minor component of somewhat poorly drained Floyd loams in the northwest (NRCS 2016). Topographically, the study site is level, and slopes do not exceed 5% grade. Land use prior to this experiment was agricultural, with corn and soybeans consistently grown in rotation at the site. |
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EM Scenario Drivers
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No scenarios presented | No scenarios presented |
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EM ID
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EM-449 |
EM-719 |
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Method Only, Application of Method or Model Run
em.detail.methodOrAppHelp
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Method + Application | Method + Application (multiple runs exist) View EM Runs |
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New or Pre-existing EM?
em.detail.newOrExistHelp
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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
em.detail.idHelp
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EM-449 |
EM-719 |
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Document ID for related EM
em.detail.relatedEmDocumentIdHelp
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Doc-335 | Doc-395 |
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EM ID for related EM
em.detail.relatedEmEmIdHelp
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EM-447 | EM-448 | EM-728 |
EM Modeling Approach
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EM ID
em.detail.idHelp
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EM-449 |
EM-719 |
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EM Temporal Extent
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2006-2007, 2010 | 2015-2017 |
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EM Time Dependence
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time-stationary | time-dependent |
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EM Time Reference (Future/Past)
em.detail.futurePastHelp
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Not applicable | Not applicable |
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EM Time Continuity
em.detail.continueDiscreteHelp
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Not applicable | discrete |
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EM Temporal Grain Size Value
em.detail.tempGrainSizeHelp
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Not applicable | 1 |
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EM Temporal Grain Size Unit
em.detail.tempGrainSizeUnitHelp
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Not applicable | Year |
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EM ID
em.detail.idHelp
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EM-449 |
EM-719 |
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Bounding Type
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Physiographic or ecological | Other |
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Spatial Extent Name
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Coastal zone surrounding St. Croix | Iowa State University Northeast Research and Demonstration Farm |
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Spatial Extent Area (Magnitude)
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100-1000 km^2 | <1 ha |
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EM ID
em.detail.idHelp
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EM-449 |
EM-719 |
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EM Spatial Distribution
em.detail.distributeLumpHelp
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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
em.detail.spGrainTypeHelp
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area, for pixel or radial feature | area, for pixel or radial feature |
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Spatial Grain Size
em.detail.spGrainSizeHelp
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10 m x 10 m | 20 ft x 28 ft |
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EM ID
em.detail.idHelp
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EM-449 |
EM-719 |
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EM Computational Approach
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Analytic | Analytic |
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EM Determinism
em.detail.deterStochHelp
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deterministic | stochastic |
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Statistical Estimation of EM
em.detail.statisticalEstimationHelp
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EM ID
em.detail.idHelp
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EM-449 |
EM-719 |
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Model Calibration Reported?
em.detail.calibrationHelp
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Yes | Not applicable |
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Model Goodness of Fit Reported?
em.detail.goodnessFitHelp
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No | Not applicable |
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Goodness of Fit (metric| value | unit)
em.detail.goodnessFitValuesHelp
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None | None |
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Model Operational Validation Reported?
em.detail.validationHelp
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Yes | No |
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Model Uncertainty Analysis Reported?
em.detail.uncertaintyAnalysisHelp
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No | Not applicable |
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Model Sensitivity Analysis Reported?
em.detail.sensAnalysisHelp
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No | Not applicable |
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Model Sensitivity Analysis Include Interactions?
em.detail.interactionConsiderHelp
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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-449 |
EM-719 |
| None |
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Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
| EM-449 |
EM-719 |
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None |
Centroid Lat/Long (Decimal Degree)
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EM ID
em.detail.idHelp
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EM-449 |
EM-719 |
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Centroid Latitude
em.detail.ddLatHelp
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17.73 | 42.93 |
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Centroid Longitude
em.detail.ddLongHelp
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-64.77 | -92.57 |
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Centroid Datum
em.detail.datumHelp
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WGS84 | WGS84 |
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Centroid Coordinates Status
em.detail.coordinateStatusHelp
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Estimated | Provided |
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EM ID
em.detail.idHelp
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EM-449 |
EM-719 |
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EM Environmental Sub-Class
em.detail.emEnvironmentalSubclassHelp
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Near Coastal Marine and Estuarine | Agroecosystems | Grasslands |
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Specific Environment Type
em.detail.specificEnvTypeHelp
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Coral reefs | Research farm in historic grassland |
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EM Ecological Scale
em.detail.ecoScaleHelp
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Ecological scale is finer than that of the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class |
Scale of differentiation of organisms modeled
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EM ID
em.detail.idHelp
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EM-449 |
EM-719 |
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EM Organismal Scale
em.detail.orgScaleHelp
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Not applicable | Community |
Taxonomic level and name of organisms or groups identified
| EM-449 |
EM-719 |
| None Available | None Available |
EnviroAtlas URL
| EM-449 |
EM-719 |
| National Hydrography Dataset Plus (NHD PlusV2) | GAP Ecological Systems |
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-449 |
EM-719 |
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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-449 |
EM-719 |
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