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-65 | EM-71 | EM-195 | EM-850 | EM-859 |
EM Short Name
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Green biomass production, Central French Alps | Community flowering date, Central French Alps | C Sequestration and De-N, Tampa Bay, FL, USA | Invertebrate community index, Alabama | ARIES Outdoor recreation, Santa Fe, NM |
EM Full Name
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Green biomass production, Central French Alps | Community weighted mean flowering date, Central French Alps | Value of Carbon Sequestration and Denitrification benefits, Tampa Bay, FL, USA | Invertebrate community index, Choctawhatchee-Pea Rivers watershed, Alabama | Artificial intelligence for Ecosystem Services (ARIES): Outdoor recreation, Santa Fe, New Mexico |
EM Source or Collection
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EU Biodiversity Action 5 | EU Biodiversity Action 5 | US EPA | None | None |
EM Source Document ID
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260 | 260 | 186 | 409 | 411 |
Document Author
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Lavorel, S., Grigulis, K., Lamarque, P., Colace, M-P, Garden, D., Girel, J., Pellet, G., and Douzet, R. | Lavorel, S., Grigulis, K., Lamarque, P., Colace, M-P, Garden, D., Girel, J., Pellet, G., and Douzet, R. | Russell, M. and Greening, H. | Bennett, H.H., Mullen, M.W., Stewart, P.M., Sawyer, J.A., and E. C. Webber | Martinez-Lopez, J.M., Bagstad, K.J., Balbi, S., Magrach, A., Voigt, B. Athanasiadis, I., Pascual, M., Willcock, S., and F. Villa. |
Document Year
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2011 | 2011 | 2013 | 2004 | 2018 |
Document Title
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Using plant functional traits to understand the landscape distribution of multiple ecosystem services | Using plant functional traits to understand the landscape distribution of multiple ecosystem services | Estimating benefits in a recovering estuary: Tampa Bay, Florida | Development of an invertebrate community index for an Alabama coastal plain watershed | Towards globally customizable ecosystem service models |
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 journal manuscript | Published journal manuscript |
EM ID
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EM-65 | EM-71 | EM-195 | EM-850 | EM-859 |
Not applicable | Not applicable | Not applicable | Not applicable |
https://integratedmodelling.org/hub/#/register ?Comment:Need to set up an account first and then can access the main integrated modelling hub page: |
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Contact Name
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Sandra Lavorel | Sandra Lavorel | M. Russell | E. Cliff Webber | Javier Martinez-Lopez |
Contact Address
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Laboratoire d’Ecologie Alpine, UMR 5553 CNRS Université Joseph Fourier, BP 53, 38041 Grenoble Cedex 9, France | Laboratoire d’Ecologie Alpine, UMR 5553 CNRS Université Joseph Fourier, BP 53, 38041 Grenoble Cedex 9, France | US EPA, Gulf Ecology Division, 1 Sabine Island Dr, Gulf Breeze, FL 32563, USA | Troy State University, 4004 Clairmont Avenue South, Birmingham, Alabama 35222 progress. | BC3-Basque Centre for Climate Change, Sede Building 1, 1st floor, Scientific Campus of the Univ. of the Basque Country, 48940 Leioa, Spain |
Contact Email
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sandra.lavorel@ujf-grenoble.fr | sandra.lavorel@ujf-grenoble.fr | Russell.Marc@epamail.epa.gov | hbennett1978@hotmail.com | javier.martinez@bc3research.org |
EM ID
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EM-65 | EM-71 | EM-195 | EM-850 | EM-859 |
Summary Description
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ABSTRACT: "Here, we propose a new approach for the analysis, mapping and understanding of multiple ES delivery in landscapes. Spatially explicit single ES models based on plant traits and abiotic characteristics are combined to identify ‘hot’ and ‘cold’ spots of multiple ES delivery, and the land use and biotic determinants of such distributions. We demonstrate the value of this trait-based approach as compared to a pure land-use approach for a pastoral landscape from the central French Alps, and highlight how it improves understanding of ecological constraints to, and opportunities for, the delivery of multiple services. Vegetative height and leaf traits such as leaf dry matter content were response traits strongly influenced by land use and abiotic environment, with follow-on effects on several ecosystem properties (e.g., green biomass production), and could therefore be used as functional markers of ES." AUTHOR'S DESCRIPTION: "Variation in green biomass production was modelled using…traits community-weighted mean (CWM) and functional divergence (FD) and abiotic variables (continuous variables; trait + abiotic) following Diaz et al. (2007). …The comparison between this model and the land-use alone model identifies the need for site-based information beyond a land use or land cover proxy, and the comparison with the land use + abiotic model assesses the value of additional ecological (trait) information…Green biomass production for each pixel was calculated and mapped using model estimates for…regression coefficients on abiotic variables and traits. For each pixel these calculations were applied to mapped estimates of abiotic variables and trait CWM and FD. This step is critically novel as compared to a direct application of the model by Diaz et al. (2007) in that we explicitly modelled the responses of trait community-weighted means and functional divergences to environment prior to evaluating their effects on ecosystem properties. Such an approach is the key to the explicit representation of functional variation across the landscape, as opposed to the use of unique trait values within each land use (see Albert et al. 2010)." | ABSTRACT: "Here, we propose a new approach for the analysis, mapping and understanding of multiple ES delivery in landscapes. Spatially explicit single ES models based on plant traits and abiotic characteristics are combined to identify ‘hot’ and ‘cold’ spots of multiple ES delivery, and the land use and biotic determinants of such distributions. We demonstrate the value of this trait-based approach as compared to a pure land-use approach for a pastoral landscape from the central French Alps, and highlight how it improves understanding of ecological constraints to, and opportunities for, the delivery of multiple services." AUTHOR'S DESCRIPTION: "Community-weighted mean date of flowering onset was modelled using mixed models with land use and abiotic variables as fixed effects (LU + abiotic model) and year as a random effect…and modelled for each 20 x 20 m pixel using GLM estimated effects for each land use category and estimated regression coefficients with abiotic variables." | AUTHOR'S DESCRIPTION: "...we examine the change in the production of ecosystem goods produced as a result of restoration efforts and potential relative cost savings for the Tampa Bay community from seagrass expansion (more than 3,100 ha) and coastal marsh and mangrove restoration (∼600 ha), since 1990… The objectives of this article are to explore the roles that ecological processes and resulting ecosystem goods have in maintaining healthy estuarine systems by (1) quantifying the production of specific ecosystem goods in a subtropical estuarine system and (2) determining potential cost savings of improved water quality and increased habitat in a recovering estuary." (pp. 2) | ABSTRACT: "Macroinvertebrates were collected from 49 randomly selected sites from first through sixth-order streams in the Choctawhatchee-Pea Rivers watershed and were identified to genus level. Thirty-eight candidate metrics were examined, and an invertebrate community index (ICI) was calibrated by eliminating metrics that failed to separate impaired from unimpaired streams. Each site was scored with those metrics, and narrative scores were assigned based on ICI scores. Least impacted sites scored significantly lower than sites impacted by row crop agriculture, cattle, and urban land uses. Conditions in the watershed suggest that the entire area has experienced degradation through past and current land use practices. An initial validation of the index was performed and is described. Additional evaluations of the index are in progress." | ABSTRACT: "Scientists, stakeholders and decision makers face trade-offs between adopting simple or complex approaches when modeling ecosystem services (ES). Complex approaches may be time- and data-intensive, making them more challenging to implement and difficult to scale, but can produce more accurate and locally specific results. In contrast, simple approaches allow for faster assessments but may sacrifice accuracy and credibility. The Artificial Intelligence for Ecosystem Services (ARIES) modeling platform has endeavored to provide a spectrum of simple to complex ES models that are readily accessible to a broad range of users. In this paper, we describe a series of five “Tier 1” ES models that users can run anywhere in the world with no user input, while offering the option to easily customize models with context-specific data and parameters. This approach enables rapid ES quantification, as models are automatically adapted to the application context. We provide examples of customized ES assessments at three locations on different continents and demonstrate the use of ARIES' spatial multicriteria analysis module, which enables spatial prioritization of ES for different beneficiary groups. The models described here use publicly available global- and continental-scale data as defaults. Advanced users can modify data input requirements, model parameters or entire model structures to capitalize on high-resolution data and context-specific model formulations. Data and methods contributed by the research community become part of a growing knowledge base, enabling faster and better ES assessment for users worldwide. By engaging with the ES modeling community to further develop and customize these models based on user needs, spatiotemporal contexts, and scale(s) of analysis, we aim to cover the full arc from simple to complex assessments, minimizing the additional cost to the user when increased complexity and accuracy are needed. " |
Specific Policy or Decision Context Cited
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None identified | None identified | Restoration of seagrass | None reported | None identified |
Biophysical Context
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Elevation ranges from 1552 to 2442 m, on predominately south-facing slopes | Elevation ranges from 1552 to 2442 m, on predominantly south-facing slopes | Recovering estuary; Seagrass; Coastal fringe; Saltwater marsh; Mangrove | 1st through 6th order streams on low elevation coastal plains | Watersheds surrounding Santa Fe and Albuquerque, New Mexico |
EM Scenario Drivers
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No scenarios presented | No scenarios presented | Habitat loss or restoration in Tampa Bay Estuary | N/A | N/A |
EM ID
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EM-65 | EM-71 | EM-195 | EM-850 | EM-859 |
Method Only, Application of Method or Model Run
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Method + Application | Method + Application | Method + Application | Method + Application | Method + Application |
New or Pre-existing EM?
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New or revised model | New or revised model | New or revised model | New or revised model | Application of existing model |
Related EMs (for example, other versions or derivations of this EM) described in ESML
EM ID
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EM-65 | EM-71 | EM-195 | EM-850 | EM-859 |
Document ID for related EM
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Doc-260 | Doc-260 | Doc-269 | None | Doc-407 | Doc-411 |
EM ID for related EM
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EM-66 | EM-68 | EM-69 | EM-70 | EM-71 | EM-79 | EM-80 | EM-81 | EM-82 | EM-83 | EM-65 | EM-66 | EM-68 | EM-69 | EM-70 | EM-79 | EM-80 | EM-81 | EM-82 | EM-83 | None | EM-848 | EM-855 | EM-856 | EM-858 |
EM Modeling Approach
EM ID
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EM-65 | EM-71 | EM-195 | EM-850 | EM-859 |
EM Temporal Extent
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2007-2009 | 2007-2008 | 1982-2010 | 2002 | 1981-2015 |
EM Time Dependence
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time-stationary | time-stationary | time-stationary | time-stationary | time-stationary |
EM Time Reference (Future/Past)
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Not applicable | Not applicable | Not applicable | Not applicable | Not applicable |
EM Time Continuity
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Not applicable | Not applicable | Not applicable | Not applicable | Not applicable |
EM Temporal Grain Size Value
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Not applicable | Not applicable | Not applicable | Not applicable | Not applicable |
EM Temporal Grain Size Unit
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Not applicable | Not applicable | Not applicable | Not applicable | Not applicable |
EM ID
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EM-65 | EM-71 | EM-195 | EM-850 | EM-859 |
Bounding Type
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Physiographic or Ecological | Physiographic or Ecological | Physiographic or Ecological | Watershed/Catchment/HUC | Watershed/Catchment/HUC |
Spatial Extent Name
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Central French Alps | Central French Alps | Tampa Bay Estuary | Choctawhatchee-Pea rivers watershed | Santa Fe Fireshed |
Spatial Extent Area (Magnitude)
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10-100 km^2 | 10-100 km^2 | 1000-10,000 km^2. | 1000-10,000 km^2. | 100-1000 km^2 |
EM ID
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EM-65 | EM-71 | EM-195 | EM-850 | EM-859 |
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 lumped (in all cases) | spatially distributed (in at least some cases) |
Spatial Grain Type
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area, for pixel or radial feature | area, for pixel or radial feature | area, for pixel or radial feature | Not applicable | area, for pixel or radial feature |
Spatial Grain Size
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20 m x 20 m | 20 m x 20 m | 1 ha | Not applicable | 30 m |
EM ID
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EM-65 | EM-71 | EM-195 | EM-850 | EM-859 |
EM Computational Approach
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Analytic | Analytic | Analytic | Analytic | Analytic |
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-65 | EM-71 | EM-195 | EM-850 | EM-859 |
Model Calibration Reported?
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No | No | Yes |
Yes ?Comment:Culled metrics that did not distinguish between impaired and unimpaired sites. |
Unclear |
Model Goodness of Fit Reported?
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Yes | Yes | No | No | No |
Goodness of Fit (metric| value | unit)
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None | None | None |
Model Operational Validation Reported?
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Yes | No | No | Yes | No |
Model Uncertainty Analysis Reported?
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No | No | No | No | No |
Model Sensitivity Analysis Reported?
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No | No | No | Yes | No |
Model Sensitivity Analysis Include Interactions?
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Not applicable | Not applicable | Not applicable | Yes | Not applicable |
EM Locations, Environments, Ecology
Terrestrial location (Classification hierarchy: Continent > Country > U.S. State [United States only])
EM-65 | EM-71 | EM-195 | EM-850 | EM-859 |
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None |
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Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
EM-65 | EM-71 | EM-195 | EM-850 | EM-859 |
None | None |
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None | None |
Centroid Lat/Long (Decimal Degree)
EM ID
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EM-65 | EM-71 | EM-195 | EM-850 | EM-859 |
Centroid Latitude
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45.05 | 45.05 | 27.95 | 31.39 | 35.86 |
Centroid Longitude
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6.4 | 6.4 | -82.47 | -85.71 | -105.76 |
Centroid Datum
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WGS84 | WGS84 | WGS84 | WGS84 | WGS84 |
Centroid Coordinates Status
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Provided | Provided | Estimated | Estimated | Estimated |
EM ID
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EM-65 | EM-71 | EM-195 | EM-850 | EM-859 |
EM Environmental Sub-Class
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Agroecosystems | Grasslands | Agroecosystems | Grasslands | Near Coastal Marine and Estuarine | Rivers and Streams | Terrestrial Environment (sub-classes not fully specified) |
Specific Environment Type
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Subalpine terraces, grasslands, and meadows | Subalpine terraces, grasslands, and meadows. | Subtropical Estuary | 1st - 6th order streams | watersheds |
EM Ecological Scale
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Not applicable | Not applicable | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class |
Scale of differentiation of organisms modeled
EM ID
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EM-65 | EM-71 | EM-195 | EM-850 | EM-859 |
EM Organismal Scale
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Community | Community | Not applicable |
Other (Comment) ?Comment:To species but focused on functional group classes |
Not applicable |
Taxonomic level and name of organisms or groups identified
EM-65 | EM-71 | EM-195 | EM-850 | EM-859 |
None Available | None Available | None Available | 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-65 | EM-71 | EM-195 | EM-850 | EM-859 |
None | None |
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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-65 | EM-71 | EM-195 | EM-850 | EM-859 |
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None |
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None | None |