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-106 | EM-319 | EM-630 | EM-936 | EM-941 |
EM Short Name
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Value of Habitat for Shrimp, Campeche, Mexico | Redfish and cold water coral (EFH), Norway | WaterWorld v2, Santa Basin, Peru | i-Tree species selector v. 4.0 | ESTIMAP - Pollination potential, Iran |
EM Full Name
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Value of Habitat for Shrimp, Campeche, Mexico | Linkage between redfish and cold water coral, Norway (essential fish habitat model) | WaterWorld v2, Santa Basin, Peru | i-Tree species selector v. 4.0 | ESTIMAP - Pollination potential, Iran |
EM Source or Collection
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None | None | None | i-Tree | None |
EM Source Document ID
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227 | 259 | 368 |
426 ?Comment:Doc# 427 is an additional source for this EM. |
434 |
Document Author
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Barbier, E. B., and Strand, I. | Foley N.S., Kahui V.K., Armstrong C.W., Van Rensburg T.M | Van Soesbergen, A. and M. Mulligan | i-Tree | Rahimi, E., Barghjelveh, S., and P. Dong |
Document Year
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1998 | 2010 | 2018 | None | 2020 |
Document Title
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Valuing mangrove-fishery linkages: A case study of Campeche, Mexico | Estimating linkages between redfish and cold water coral on the Norwegian coast | Potential outcomes of multi-variable climate change on water resources in the Santa Basin, Peru | i-Tree Species Selector User's Manual v. 4.0 | Using the Lonsdorf and ESTIMAP models for large-scale pollination Using the Lonsdorf and ESTIMAP models for large-scale pollination mapping (Case study: Iran) |
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 | Webpage | Published journal manuscript |
EM ID
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EM-106 | EM-319 | EM-630 | EM-936 | EM-941 |
Not applicable | Not applicable | www.policysupport.org/waterworld | https://species.itreetools.org/ | Not applicable | |
Contact Name
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E.B. Barbier | Naomi S. Foley | Arnout van Soesbergen |
Not reported ?Comment:send comments through any of the means listed on the i-Tree support page: http://www.itreetools.org/support/. |
Ehsan Rahini |
Contact Address
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Environment Department, University of York, York YO1 5DD, UK | Dept. of Economics and Management, Univeristy of Tromso, Norway | Environmental Dynamics Research Group, Dept. of Geography, King's College London, Strand, London WC2R 2LS, UK | Not reported | Environmental Sciences Research Institute, Shahid Beheshti University, Tehran, Iran |
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Not reported | naomifoley@gmail.com | arnout.van_soesbergen@kcl.ac.uk | info@itreetools.org | ehsanrahimi666@gmail.com |
EM ID
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EM-106 | EM-319 | EM-630 | EM-936 | EM-941 |
Summary Description
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AUTHOR'S DESCRIPTION: "We assume throughout that shrimp harvesting occurs through open access management that yields production which is exported internationally, and we modify a standard open access fishery model to account explicitly for the effect of the mangrove area on carrying capacity and thus production.We derive the conditions determining the long-run equilibrium of the model, including the comparative static effects of a change in mangrove area, on this equilibrium. Through regressing a relationship between shrimp harvest, effort and mangrove area over time, we estimate parameters based on the combinations of the bioeconomic parameters of the model determining the comparative statics. By incorporating additional economic data, we are able to simulate an estimate of the effect of changes in mangrove area in Laguna de Terminos on the production and value of shrimp harvests in Campeche state." (153) | ABSTRACT: "…This paper applies the production function approach to estimate the link between cold water corals and redfish in Norway. Both the carrying capacity and growth rate of redfish are found to be functions of cold water coral habitat and thus cold water corals can be considered an essential fish habitat…The essential habitat model shows the best fit to the data…" AUTHOR'S DESCRIPTION: "…the EFH model presented by Barbier and Strand (1998), in which the habitat is considered essential to the stock; i.e., if the habitat declines to zero the fish stock will perish…based on the Gordon-Schaefer model, which is a single-species biomass model, where effort is the control variable and fish stock is the state variable. In the case of habitat-fisheries interactions, such as in our case, a second state variable is introduced, the habitat (CWC)…Scientists have stimated that 30-50% of CWC habitat has been damaged (Fossa, Mortensen, and Furevik 2002. Working within these bounds, we empirically estimate the relationship between CWC as a habitat and a fish stock..." | ABSTRACT: "Water resources in the Santa basin in the Peruvian Andes are increasingly under pressure from climate change and population increases. Impacts of temperature-driven glacier retreat on stream flow are better studied than those from precipitation changes, yet present and future water resources are mostly dependent on precipitation which is more difficult to predict with climate models. This study combines a broad range of projections from climate models with a hydrological model (WaterWorld), showing a general trend towards an increase in water availability due to precipitation increases over the basin. However, high uncertainties in these projections necessitate the need for basin-wide policies aimed at increased adaptability." AUTHOR'S DESCRIPTION: "WaterWorld is a fully distributed, process-based hydrological model that utilises remotely sensed and globally available datasets to support hydrological analysis and decision-making at national and local scales globally, with a particular focus on un-gauged and/or data-poor environments, which makes it highly suited to this study. The model (version 2) currently runs on either 10 degree tiles, large river basins or countries at 1-km2 resolution or 1 degree tiles at 1-ha resolution utilising different datasets. It simulates a hydrological baseline as a mean for the period 1950-2000 and can be used to calculate the hydrological impact of scenarios of climate change, land use change, land management options, impacts of extractives (oil & gas and mining) and impacts of changes in population and demography as well as combinations of these. The model is ‘self parameterising’ (Mulligan, 2013a) in the sense that all data required for model application anywhere in the world is provided with the model, removing a key barrier to model application. However, if users have better data than those provided, it is possible to upload these to WaterWorld as GIS files and use them instead. Results can be viewed visually within the web browser or downloaded as GIS maps. The model’s equations and processes are described in more detail in Mulligan and Burke (2005) and Mulligan (2013b). The model parameters are not routinely calibrated to observed flows as it is designed for hydrological scenario analysis in which the physical basis of its parameters must be retained and the model is also often used in un-gauged basins. Calibration is inappropriate under these circumstances (Sivapalan et al., 2003). The freely available nature of the model means that anyone can apply it and replicate the results shown here. WaterWorld’s (V2) snow and ice module is capable of simulating the processes of melt water production, snow fall and snow pack, making this version highly suited to the current application. The model component is based on a full energy-balance for snow accumulation and melting based on Walter et al., (2005) with input data provided globally by the SimTerra database (Mulligan, 2011) upon which the model r | ABSTRACT: "The Species Selector is a free-standing i-Tree utility that ranks tree species based on their environmental benefits at maturity. As such, it complements existing tree selection programs that rank species based on esthetics or other features. Species are selected based on three types of information. First, hardiness is considered. The hardiness zone is determined based on state and city, and all species that are not sufficiently hardy are eliminated from consideration. Second, mature height is considered. Users are asked to specify minimum and maximum heights, and species outside of that range are eliminated. Finally, eight environmental factors are considered in the rankings created by the Species Selector: • Air pollution removal • Air temperature reduction • Ultraviolet radiation reduction • Carbon storage • Pollen allergenicity • Building energy conservation • Wind reduction • Stream flow reduction (stormwater management). Users are asked to rank the importance of each of these factors on a scale of 0 to 10. The combination of hardiness, mature height, and desired functionality produces a ranked list of appropriate species from an initial database of about 1,600 species. The large species database covers a broad range of native, naturalized and exotic trees, some of which are commonly planted in urban areas. Since only city hardiness zone, tree height and user functional preferences are used to produce the list, there may well be many species on the list that are unsuitable to the local context for a variety of reasons. A species may have particular structural, drainage, sun, pest, or soil pH limitations that should exclude it from use. Furthermore, since many native and exotic species are included, items may appear that are simply not available in the local trade. For these reasons, the list should be considered a beginning rather than an end. The list will need to be whittled down to meet local needs and limitations. Relevant cultural needs should be taken into account as well. The result will be a list of recommended species suited for local use that maximizes environmental services." | Abstract: ". ..we used the ESTIMAP model to improve the results of the Lonsdorf model. For this, we included the effects of roads, railways, rivers, wetlands, lakes, altitude, climate, and ecosystem boundaries in the ESTIMAP modeling and compared the results with the Lonsdorf model. The results of the Lonsdorf model showed that the majority of Iran had a very low potential for providing pollination service and only three percent of the northern and western parts of Iran had high potential. However, the results of the ESTIMAP model showed that 16% of Iran had a high potential to provide pollination that covers most of the northern and southern parts of the country. The results of the ESTIMAP model for pollination mapping in Iran showed the Lonsdorf model of estimating pollination service can be improved through considering other relevant factors." |
Specific Policy or Decision Context Cited
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None identified | None identified | None identified | None identified | None reported |
Biophysical Context
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Gulf of Mexico; mangrove-lagoon system | Continental slope | Large river valley located on the western slope of the Peruvian Andes between the Cordilleras Blanca and Negra. Precipitation is distinctly seasonal. | No additional description provided | None additional |
EM Scenario Drivers
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No scenarios presented | Estimated impact differences due to fishing effort; minimum (30%), and maximum (50%) degredation (reduction) in coral reef area. | Scenarios base on high growth and 3.5oC warming by 2100, and scenarios based on moderate growth and 2.5oC warming by 2100 | No scenarios presented | N/A |
EM ID
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EM-106 | EM-319 | EM-630 | EM-936 | EM-941 |
Method Only, Application of Method or Model Run
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Method + Application | Method + Application | Method + Application (multiple runs exist) | Method Only | Method + Application |
New or Pre-existing EM?
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New or revised model | Application of existing model | Application of existing 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-106 | EM-319 | EM-630 | EM-936 | EM-941 |
Document ID for related EM
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None | Doc-227 | None | Doc-427 | Doc-432 |
EM ID for related EM
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EM-185 | EM-319 | EM-106 | None | None | EM-939 |
EM Modeling Approach
EM ID
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EM-106 | EM-319 | EM-630 | EM-936 | EM-941 |
EM Temporal Extent
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1980-1990 | 1986-2002 | 1950-2071 | Not applicable | 2020 |
EM Time Dependence
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time-stationary | time-stationary | time-dependent | Not applicable | time-stationary |
EM Time Reference (Future/Past)
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Not applicable | Not applicable | both | Not applicable | Not applicable |
EM Time Continuity
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Not applicable | Not applicable | discrete | Not applicable | Not applicable |
EM Temporal Grain Size Value
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Not applicable | Not applicable | 1 | Not applicable | Not applicable |
EM Temporal Grain Size Unit
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Year | Not applicable | Month | Not applicable | Not applicable |
EM ID
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EM-106 | EM-319 | EM-630 | EM-936 | EM-941 |
Bounding Type
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Physiographic or Ecological | Physiographic or ecological | Watershed/Catchment/HUC | Not applicable | Geopolitical |
Spatial Extent Name
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Laguna de Terminos Mangrove system | Norwegian Sea (ICES areas I and II) | Santa Basin | Not applicable | Iran |
Spatial Extent Area (Magnitude)
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100-1000 km^2 | 1000-10,000 km^2. | 10,000-100,000 km^2 | Not applicable | >1,000,000 km^2 |
EM ID
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EM-106 | EM-319 | EM-630 | EM-936 | EM-941 |
EM Spatial Distribution
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spatially distributed (in at least some cases) | spatially lumped (in all cases) | spatially distributed (in at least some cases) | Not applicable |
spatially distributed (in at least some cases) ?Comment:Varies by inputs, but results are for areas of country |
Spatial Grain Type
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area, for pixel or radial feature | Not applicable | area, for pixel or radial feature | Not applicable | area, for pixel or radial feature |
Spatial Grain Size
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1 km x 1 km | Not applicable | 1 km2 | Not applicable | ha^2 |
EM ID
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EM-106 | EM-319 | EM-630 | EM-936 | EM-941 |
EM Computational Approach
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Analytic | Analytic | * | Analytic | Numeric |
EM Determinism
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deterministic | deterministic | deterministic | deterministic | deterministic |
Statistical Estimation of EM
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None |
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EM ID
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EM-106 | EM-319 | EM-630 | EM-936 | EM-941 |
Model Calibration Reported?
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Yes | Yes | No | Not applicable | No |
Model Goodness of Fit Reported?
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Yes | Yes | No | Not applicable | No |
Goodness of Fit (metric| value | unit)
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None | None | None |
Model Operational Validation Reported?
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No | No | Yes | Not applicable | No |
Model Uncertainty Analysis Reported?
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Yes | No | No | Not applicable | No |
Model Sensitivity Analysis Reported?
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Yes | Yes | No | Not applicable | No |
Model Sensitivity Analysis Include Interactions?
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Unclear | Yes | Not applicable | Not applicable | Not applicable |
EM Locations, Environments, Ecology
Terrestrial location (Classification hierarchy: Continent > Country > U.S. State [United States only])
EM-106 | EM-319 | EM-630 | EM-936 | EM-941 |
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None | None |
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Comment:Model for Iran - no form preset id for country |
Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
EM-106 | EM-319 | EM-630 | EM-936 | EM-941 |
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None | None | None |
Centroid Lat/Long (Decimal Degree)
EM ID
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EM-106 | EM-319 | EM-630 | EM-936 | EM-941 |
Centroid Latitude
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18.61 | 70 | -9.05 | Not applicable | 32.29 |
Centroid Longitude
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-91.55 | 10 | -77.81 | Not applicable | 53.68 |
Centroid Datum
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WGS84 | WGS84 | WGS84 | Not applicable | WGS84 |
Centroid Coordinates Status
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Estimated | Estimated | Estimated | Not applicable | Estimated |
EM ID
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EM-106 | EM-319 | EM-630 | EM-936 | EM-941 |
EM Environmental Sub-Class
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Near Coastal Marine and Estuarine | Near Coastal Marine and Estuarine | Open Ocean and Seas | None | Created Greenspace | Terrestrial Environment (sub-classes not fully specified) |
Specific Environment Type
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Mangrove | cold water coral reefs | tropical, coastal to montane | Urban greenspace | terrestrial land types |
EM Ecological Scale
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Ecological scale is finer than that of the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Other or unclear (comment) | Ecological scale corresponds to 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-106 | EM-319 | EM-630 | EM-936 | EM-941 |
EM Organismal Scale
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Guild or Assemblage | Guild or Assemblage | Not applicable | Species | Not applicable |
Taxonomic level and name of organisms or groups identified
EM-106 | EM-319 | EM-630 | EM-936 | EM-941 |
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None Available | None Available |
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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-106 | EM-319 | EM-630 | EM-936 | EM-941 |
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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-106 | EM-319 | EM-630 | EM-936 | EM-941 |
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None | None |
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