The Wetland Object Model (WOM): a geographic object-based simulation framework for studies in wetland ecohydrology
Bibliographic record
Abstract
The complexities of intra-wetland hydrological processes have not yet been adequately addressed using traditional hydrological modelling tools. These processes demand the development and testing of a new spatially explicit simulation framework for modelling wetland ecosystems that exhibit multi-scale heterogeneities. The purpose of this work was to design a prototypical, ecohydrological modelling framework specific to wetland environments using numerical and spatial modelling techniques. The Wetland Object Model (WOM) was developed entirely in Java, an object-oriented programming language. Object-oriented principles and design patterns enable the development of fundamental data structures that serve as interchangeable building blocks for constructing fine-scale models of wetland hydrology. These data-structures were used to illustrate three examples of non-topographically driven hydrodynamics in a simulated wetland environment. An overview the WOM prototype is provided with emphasis on how the object model can be used for the characterization of complex structure, function and hydrological boundary conditions within wetland environments.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".