paper update

paper.bib

@@ -126,3 +126,34 @@ @software{DisSModelSysDyn
   publisher = {GitHub},
   url       = {https://github.com/DisSModel/dissmodel-sysdyn}
 }
+
+@article{Pontius2008,
+  author  = {Pontius, Robert Gilmore and Peethambaram, Sneha and Castella, Jean-Christophe},
+  title   = {Comparison of Three Maps at Multiple Resolutions: a Case Study of Land Change Simulation in Cho Don District, Vietnam},
+  journal = {Annals of the Association of American Geographers},
+  year    = {2008},
+  volume  = {98},
+  number  = {1},
+  pages   = {28--49}
+}
+
+
+@article{Willmott2005,
+  author  = {Willmott, Cort J. and Matsuura, Kenji},
+  title   = {Advantages of the mean absolute error ({MAE}) over the root mean
+             square error ({RMSE}) in assessing average model performance},
+  journal = {Climate Research},
+  year    = {2005},
+  volume  = {30},
+  pages   = {79--82}
+}
+
+
+@article{Veldkamp1996,
+  author  = {Veldkamp, A. and Fresco, L. O.},
+  title   = {{CLUE}: a conceptual model to study the conversion of land use and its effects},
+  journal = {Ecological Modelling},
+  year    = {1996},
+  volume  = {85},
+  pages   = {253--270}
+}

paper.md

@@ -75,7 +75,8 @@ Beyond simulation execution, reproducibility is a first-class concern in DisSMod
 The `executor` module provides a standardised lifecycle — `validate → load → run → save`
 — that captures provenance metadata (input checksums, parameters, timing, output
 paths) in an `ExperimentRecord` object automatically generated for every run. This
-design ensures that results produced locally or via CLI are fully traceable without additional instrumentation by the modeller.
+design ensures that results produced locally or via CLI are fully traceable without
+additional instrumentation by the modeller.
 
 ## State of the Field
 
@@ -138,13 +139,19 @@ explicit `fmt` argument. For cloud deployments, the same API resolves
 interactive dashboards, and `RasterMap` for step-by-step raster rendering in both
 headless and interactive modes.
 
-The extensibility of DisSModel's class hierarchy has already produced two domain
-packages distributed independently on PyPI: `dissmodel-ca` [@DisSModelCA], which
-provides ready-to-use Cellular Automata patterns built on `RasterCellularAutomaton`,
-and `dissmodel-sysdyn` [@DisSModelSysDyn], which adds System Dynamics compartmental
-models as first-class DisSModel components. Both packages follow the same
-`ModelExecutor` contract and serve as reference implementations for researchers
-building their own domain extensions.
+The extensibility of DisSModel's class hierarchy has already produced domain
+packages distributed independently on PyPI. `dissmodel-ca` [@DisSModelCA] provides
+ready-to-use Cellular Automata patterns built on `RasterCellularAutomaton`.
+`dissmodel-sysdyn` [@DisSModelSysDyn] adds System Dynamics compartmental models as
+first-class DisSModel components. `DisSLUCC-Continuous` [@DisSLUCCContinuous]
+implements the continuous LUCC modeling components of the LUCCME framework — Demand,
+Potential, and Allocation — following the three-pillar architecture proposed by
+@Veldkamp1996 and @Verburg2004, on both vector and raster substrates and following
+the same `ModelExecutor` contract. This last package establishes an explicit Python
+counterpart to the original TerraME/LUCCME stack, where DisSModel occupies the role
+of TerraME and DisSLUCC-Continuous occupies the role of LUCCME. All three packages
+serve as reference implementations for researchers building their own domain
+extensions.
 
 ## Performance
 
@@ -173,8 +180,9 @@ Bezerra et al. [@Bezerra2013], who discussed the integration of remote sensing a
 computational models to assess sea-level rise impacts on mangrove ecosystems. Building
 on this framework, the `coastal-dynamics` package [@CoastalDynamics] implements
 coupled flood and mangrove succession models over the same spatial domain on both
-substrates. Running 20 simulation steps over a 60×60 synthetic grid (3,600 cells,
-EPSG:31984) produces the following results:
+substrates. Outputs are categorical land-use and soil classes; match percentage is
+therefore the appropriate primary metric [@Pontius2008]. Running 20 simulation steps
+over a 60×60 synthetic grid (3,600 cells, EPSG:31984) produces the following results:
 
 | Band | Match % | MAE | RMSE | Max Error | Cells |
 |------|--------:|----:|-----:|----------:|------:|
@@ -190,6 +198,27 @@ generated by this run captured the full execution provenance automatically:
 load phase 2.898 s (49.4%), run phase 2.972 s (50.6%), input SHA-256 checksum,
 and artifact paths — with zero additional instrumentation by the modeller.
 
+**Domain validation — DisSLUCC-Continuous LUCC model.** The `DisSLUCC-Continuous`
+package implements the continuous CLUE-like allocation algorithm [@Veldkamp1996],
+in which each cell holds a fractional land-use proportion rather than a discrete
+class. For continuous outputs, fixed-threshold match statistics are not meaningful for
+real-valued spatial data [@Pontius2008]; MAE is the appropriate primary metric,
+as it provides a directly interpretable measure of average model error [@Willmott2005]. Running 6 simulation steps over the Lab1 study area (6,574 cells)
+and comparing against a TerraME/LUCCME reference result produces the following:
+
+| Comparison | MAE | RMSE | Max Error | Cells |
+|---|----:|-----:|----------:|------:|
+| Vector vs TerraME | 0.003583 | 0.006188 | 0.027355 | 6,574 |
+| Raster vs TerraME | 0.003583 | 0.006188 | 0.027355 | 6,574 |
+| Vector vs Raster  | 0.000000 | 0.000000 | 0.000000 | 6,574 |
+
+Both substrates reproduce the TerraME/LUCCME reference with a MAE of 0.0036 in
+[0,1] scale — consistent with expected floating-point divergence between independent
+Lua and Python implementations of the same continuous allocation algorithm. Vector
+and raster substrates are numerically identical (MAE = 0.000), confirming that the
+3.6× performance gain of the raster substrate (39.9 ms/step vs 143.2 ms/step)
+introduces no algorithmic divergence.
+
 ## Research Impact Statement
 
 DisSModel provides a critical bridge for the environmental modeling community. By
@@ -199,11 +228,20 @@ framework has already been instrumental in academic research at the **LambdaGeo*
 group (UFMA), supporting studies on mangrove ecosystem dynamics and land-use change,
 building upon established spatial modeling practices [@Verburg2004; @SantosJunior2025].
 
+The emergence of independent domain packages — `dissmodel-ca`, `dissmodel-sysdyn`,
+`DisSLUCC-Continuous`, and `coastal-dynamics` — without modifications to the core
+framework demonstrates that the `ModelExecutor` contract is stable and sufficient for
+real-world modeling requirements. This is further evidenced by the DisSModel
+Platform, a distributed execution environment currently under development that
+already orchestrates both `DisSLUCC-Continuous` and `coastal-dynamics` in a shared
+test infrastructure, running each through the same job queue without any change to
+their scientific code. The platform validates the central design principle of
+DisSModel: that simulation science should not need to be rewritten to run in
+production.
+
 This architecture positions DisSModel as the simulation layer in the Brazilian Earth
 Observation stack — complementary to SITS [@Simoes2021] for present-state land
-classification and the Brazil Data Cube [@Ferreira2020] for satellite data
-access — and is designed to scale toward Pangeo-style ensemble runs via Dask
-delayed operations over shared loaded datasets.
+classification and the Brazil Data Cube [@Ferreira2020] for satellite data access.
 
 ## AI Usage Disclosure
 
@@ -212,4 +250,4 @@ Jules) to assist with structuring documentation, synthesising prior work, and
 generating submission checklists. All outputs were reviewed and validated by the
 human authors.
 
-## References
+## References