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
EM ID
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EM-97 |
EM-224 ![]() |
EM-465 | EM-493 | EM-943 |
EM Short Name
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AnnAGNPS, Kaskaskia River watershed, IL, USA | FORCLIM v2.9, West Cascades, OR, USA | Pharmaceutical product potential, St. Croix, USVI | EnviroAtlas-Carbon sequestered by trees | Visitation to natural areas, New England, USA |
EM Full Name
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AnnAGNPS (Annualized Agricultural Non-Point Source Pollution Model), Kaskaskia River watershed, IL, USA | FORCLIM (FORests in a changing CLIMate) v2.9, West Cascades, OR, USA | Relative pharmaceutical product potential (on reef), St. Croix, USVI | US EPA EnviroAtlas - Total carbon sequestered by tree cover; Example is shown for Durham NC and vicinity, USA | Estimating natural area use with cell phone data, Narragansett Beach, New England, USA |
EM Source or Collection
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US EPA | US EPA | US EPA | US EPA | EnviroAtlas | i-Tree | US EPA |
EM Source Document ID
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137 |
23 ?Comment:Related document ID 22 is a secondary source providing tree species specific parameters in appendix. |
335 |
223 ?Comment:Additional source: I-tree Eco (doc# 345). |
436 |
Document Author
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Yuan, Y., Mehaffey, M. H., Lopez, R. D., Bingner, R. L., Bruins, R., Erickson, C. and Jackson, M. | Busing, R. T., Solomon, A. M., McKane, R. B. and Burdick, C. A. | Yee, S. H., Dittmar, J. A., and L. M. Oliver | US EPA Office of Research and Development - National Exposure Research Laboratory | Merrill, N.H., Atkinson, S.F., Mulvaney, K.K., Mazzotta, K.K., and J. Bousquin |
Document Year
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2011 | 2007 | 2014 | 2013 | 2020 |
Document Title
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AnnAGNPS model application for nitrogen loading assessment for the Future Midwest Landscape study | Forest dynamics in Oregon landscapes: evaluation and application of an individual-based model | Comparison of methods for quantifying reef ecosystem services: A case study mapping services for St. Croix, USVI | EnviroAtlas - Featured Community | Using data derived from cellular phone locations to estimate visitation to natural areas: An application to water recreation in New England, USA |
Document Status
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Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published |
Comments on Status
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Published journal manuscript | Published journal manuscript | Published journal manuscript | Published on US EPA EnviroAtlas website | Published journal manuscript |
EM ID
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EM-97 |
EM-224 ![]() |
EM-465 | EM-493 | EM-943 |
https://www.ars.usda.gov/southeast-area/oxford-ms/national-sedimentation-laboratory/watershed-physical-processes-research/docs/annagnps-pollutant-loading-model/ | Not applicable | Not applicable | https://www.epa.gov/enviroatlas | https://github.com/USEPA/Recreation_Benefits.git | |
Contact Name
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Yongping Yuan | Richard T. Busing | Susan H. Yee | EnviroAtlas Team | Nathaniel Merrill |
Contact Address
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U.S. Environmental Protection Agency Office of Research and Development, Environmental Sciences Division, 944 East Harmon Ave., Las Vegas, NV 89119, USA | U.S. Geological Survey, 200 SW 35th Street, Corvallis, Oregon 97333 USA | US EPA, Office of Research and Development, NHEERL, Gulf Ecology Division, Gulf Breeze, FL 32561, USA | Not reported | Atlantic Coastal Environmental Sciences Division, U.S. Environmental Protection Agency, Office of Research and Development, Center for Environmental Measurement and Modeling, Narragansett, Rhode Island, United States of America, |
Contact Email
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yuan.yongping@epa.gov | rtbusing@aol.com | yee.susan@epa.gov | enviroatlas@epa.gov | merrill.nathaniel@epa.gov |
EM ID
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EM-97 |
EM-224 ![]() |
EM-465 | EM-493 | EM-943 |
Summary Description
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AUTHORS' DESCRIPTION: "AnnAGNPS is an advanced simulation model developed by the USDA-ARS and Natural Resource Conservation Services (NRCS) to help evaluate watershed response to agricultural management practices. It is a continuous simulation, daily time step, pollutant loading model designed to simulate water, sediment and chemical movement from agricultural watersheds.p. 198" | ABSTRACT: "The FORCLIM model of forest dynamics was tested against field survey data for its ability to simulate basal area and composition of old forests across broad climatic gradients in western Oregon, USA. The model was also tested for its ability to capture successional trends in ecoregions of the west Cascade Range…The simulation of both stand-replacing and partial-stand disturbances across western Oregon improved agreement between simulated and actual data." AUTHOR'S DESCRIPTION: "An analysis of forest successional dynamics was performed on ecoregions 4a and 4b, which cover the south Santiam watershed area selected for intensive study. In each of these two ecoregions, a set of 20 simulated sites was compared to survey plot data summaries. Survey data were analysed by stand age class and simulations of corresponding ages. The statistical methods described…were applied in comparison of actual with simulated forest composition and total basal area by age class. Separate simulations were run with and without fire." | 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…When data on sponge diversity is unavailable, benthic habitat coverages may be used to estimate relative magnitudes of sponge diversity and abundance as an indicator of potential pharmaceutical production (Mumby et al., 2008). For each grid cell, we estimated the contribution of coral reefs to potential pharmaceutical production as the overall weighted average of relative magnitudes of contribution across habitat types within that grid cell: Pharmaceutical product potential = ΣiciMi where ci is the fraction of area within each grid cell for each habitat type i (dense, medium dense, or sparse seagrass, mangroves, sand, macroalgae, A. palmata, Montastraea reef, patch reef, and dense or sparse gorgonians), and Mi is the relative magnitude of sponge diversity associated with each habitat." | The Total carbon sequestered by tree cover model has been used to create coverages for several US communities. An example for Durham, NC is shown in this entry. DATA FACT SHEET: "This EnviroAtlas community map estimates the total metric tons (mt) of carbon that are removed annually from the atmosphere and sequestered in the above-ground biomass of trees in each census block group. The data for this map were derived from a high-resolution tree cover map developed by EPA. Within each census block group derived from U.S. Census data, the total amount of tree cover (m2) was determined using this remotely-sensed land cover data. The USDA Forest Service i-Tree model was used to estimate the annual carbon sequestration rate from state-based rates of kgC/m2 of tree cover/year. The state rates vary based on length of growing season and range from 0.168 kgC/m2 of tree cover/year (Alaska) to 0.581 kgC/m2 of tree cover/year (Hawaii). The national average rate is 0.306 kgC/m2 of tree cover/year. These national and state values are based on field data collected and analyzed in several cities by the U.S. Forest Service. These values were converted to metric tons of carbon removed and sequestered per year by census block group." | ABSTRACT: "We introduce and validate the use of commercially available human mobility datasets based on cell phone locations to estimate visitation to natural areas. By combining this data with on-the-ground observations of visitation to water recreation areas in New England, we fit a model to estimate daily visitation for four months to more than 500 sites. The results show the potential for this new big data source of human mobility to overcome limitations in traditional methods of estimating visitation and to provide consistent information at policy-relevant scales. However, the data providers’ opaque and rapidly developing methods for processing locational information required a calibration and validation against data collected by traditional means to confidently reproduce the desired estimates of visitation. We found that with this calibration, the high-resolution information in both space and time provided by cell phone location-derived data creates opportunities for developing next-generation models of human interactions with the natural environment. " |
Specific Policy or Decision Context Cited
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Not reported | None Identified | None identified | None identified | None identified |
Biophysical Context
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Upper Mississipi River basin, elevation 142-194m, | West Cascade lowlands (4a), and west Cascade montane (4b) ecoregions | No additional description provided | No additional description provided | Natural area water bodies |
EM Scenario Drivers
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Alternative agricultural land use (type and crop management (fertilizer application) towards a future biofuel target | Two scenarios modelled, forests with and without fire | No scenarios presented | No scenarios presented | N/A |
EM ID
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EM-97 |
EM-224 ![]() |
EM-465 | EM-493 | EM-943 |
Method Only, Application of Method or Model Run
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Method + Application |
Method + Application (multiple runs exist) View EM Runs ?Comment:Related document ID 22 is a secondary source providing tree species specific parameters in appendix. |
Method + Application | Method + Application | Method + Application |
New or Pre-existing EM?
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New or revised model | Application of existing model | Application of existing model | Application of existing model | New or revised model |
Related EMs (for example, other versions or derivations of this EM) described in ESML
EM ID
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EM-97 |
EM-224 ![]() |
EM-465 | EM-493 | EM-943 |
Document ID for related EM
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Doc-142 | Doc-22 | Doc-23 | None | Doc-345 | None |
EM ID for related EM
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None | EM-146 | EM-208 | EM-186 | None | None | None |
EM Modeling Approach
EM ID
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EM-97 |
EM-224 ![]() |
EM-465 | EM-493 | EM-943 |
EM Temporal Extent
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1980-2006 | >650 yrs | 2006-2007, 2010 | 2010-2013 | 2017 |
EM Time Dependence
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time-stationary | time-dependent | time-stationary | time-stationary | time-dependent |
EM Time Reference (Future/Past)
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Not applicable | past time | Not applicable | Not applicable | past time |
EM Time Continuity
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Not applicable | discrete | Not applicable | Not applicable | discrete |
EM Temporal Grain Size Value
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Not applicable | 1 | Not applicable | Not applicable | 1 |
EM Temporal Grain Size Unit
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Not applicable | Year | Not applicable | Not applicable | Day |
EM ID
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EM-97 |
EM-224 ![]() |
EM-465 | EM-493 | EM-943 |
Bounding Type
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Watershed/Catchment/HUC | Physiographic or ecological | Physiographic or ecological | Geopolitical | Point or points |
Spatial Extent Name
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East Fork Kaskaskia River watershed basin | West Cascades, Oregon | Coastal zone surrounding St. Croix | Durham NC and vicinity | Cape Cod |
Spatial Extent Area (Magnitude)
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100-1000 km^2 | 100-1000 km^2 | 100-1000 km^2 | 100-1000 km^2 | 1000-10,000 km^2. |
EM ID
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EM-97 |
EM-224 ![]() |
EM-465 | EM-493 | EM-943 |
EM Spatial Distribution
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spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) |
spatially distributed (in at least some cases) ?Comment:Census block groups |
spatially distributed (in at least some cases) |
Spatial Grain Type
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length, for linear feature (e.g., stream mile) | area, for pixel or radial feature | area, for pixel or radial feature | other (specify), for irregular (e.g., stream reach, lake basin) | other (specify), for irregular (e.g., stream reach, lake basin) |
Spatial Grain Size
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1 km^2 | 0.08 ha | 10 m x 10 m | irregular | water feature edge (beach) |
EM ID
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EM-97 |
EM-224 ![]() |
EM-465 | EM-493 | EM-943 |
EM Computational Approach
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Numeric | Numeric | Analytic | Numeric | Numeric |
EM Determinism
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deterministic | deterministic | deterministic | deterministic | deterministic |
Statistical Estimation of EM
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EM ID
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EM-97 |
EM-224 ![]() |
EM-465 | EM-493 | EM-943 |
Model Calibration Reported?
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No | No | Yes | No | Yes |
Model Goodness of Fit Reported?
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No | No | No | No |
Yes ?Comment:Random forest model performance statistics |
Goodness of Fit (metric| value | unit)
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None | None | None | None |
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Model Operational Validation Reported?
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Yes | Yes | Yes | No | Yes |
Model Uncertainty Analysis Reported?
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Yes | No | No | No | Unclear |
Model Sensitivity Analysis Reported?
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Unclear | No | No | No | Yes |
Model Sensitivity Analysis Include Interactions?
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Not applicable | Not applicable | Not applicable | Not applicable | Unclear |
EM Locations, Environments, Ecology
Terrestrial location (Classification hierarchy: Continent > Country > U.S. State [United States only])
EM-97 |
EM-224 ![]() |
EM-465 | EM-493 | EM-943 |
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None |
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Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
EM-97 |
EM-224 ![]() |
EM-465 | EM-493 | EM-943 |
None | None |
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None | None |
Centroid Lat/Long (Decimal Degree)
EM ID
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EM-97 |
EM-224 ![]() |
EM-465 | EM-493 | EM-943 |
Centroid Latitude
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38.69 | 44.24 | 17.73 | 35.99 | 41.72 |
Centroid Longitude
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-89.1 | -122.24 | -64.77 | -78.96 | -70.29 |
Centroid Datum
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WGS84 | WGS84 | WGS84 | None provided | WGS84 |
Centroid Coordinates Status
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Provided | Estimated | Estimated | Estimated | Estimated |
EM ID
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EM-97 |
EM-224 ![]() |
EM-465 | EM-493 | EM-943 |
EM Environmental Sub-Class
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Agroecosystems | Forests | Near Coastal Marine and Estuarine | Created Greenspace | Atmosphere | Lakes and Ponds | Near Coastal Marine and Estuarine |
Specific Environment Type
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Row crop agriculture in Kaskaskia river basin | Primarily conifer forest | Coral reefs | Urban and vicinity | beaches |
EM Ecological Scale
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Ecological scale corresponds to the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class |
Scale of differentiation of organisms modeled
EM ID
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EM-97 |
EM-224 ![]() |
EM-465 | EM-493 | EM-943 |
EM Organismal Scale
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Not applicable | Species | Guild or Assemblage | Not applicable | Not applicable |
Taxonomic level and name of organisms or groups identified
EM-97 |
EM-224 ![]() |
EM-465 | EM-493 | EM-943 |
None Available |
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None Available | None Available |
EnviroAtlas URL
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-97 |
EM-224 ![]() |
EM-465 | EM-493 | EM-943 |
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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-97 |
EM-224 ![]() |
EM-465 | EM-493 | EM-943 |
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None |
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None |
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