Modelling Peatland Productivity by Water Table Depth or Near-Surface Water Contents via the DIMONA Online Platform
Bibliographic record
Abstract
This study extends our previous work showing that a process-based (PB) model, the DIMONA PB model, could accurately simulate peatland soil water dynamics when driven by water table depth, dWT, or by near surface soil water contents, θ. Here, we explore the model’s ability to simulate the peatland canopy photosynthesis, growth, biomass, height, and gross primary productivity (GPP) of vascular plants and bryophytes—thus ecosystem GPP—using either of these drivers. The DIMONA PB model is embedded into the DIMONA online modelling platform, a web application capable of ingesting data from the Internet and performing machine learning (ML) modelling and Internet of Things (IoT) modelling complementary to PB modelling. We test whether the DIMONA PB model, driven by dWT (Hypothesis 1) and by near-surface θ (Hypothesis 2), can successfully simulate peatland ecosystem GPP at the Mer Bleue bog (Ontario, Canada). Two model runs were generated, one driven by dWT and another by near-surface θ. Both model runs performed with similar accuracy. Data fit for simulated on observed GPP reached 0.72 for R2, 1.7 umol CO2 m−2 s−1 for RMSE, and 0.88 for Willmott’s index of agreement at an hourly time step and 0.91, 0.8 g C m−2 d−1, and 0.92, respectively, at a daily time step. We use the output from the two model runs to examine whether the model’s modifiers (i.e., equations) for water control can capture the specifics of contrasting hydrological conditions on peatland GPP (Hypothesis 3). Both model runs closely simulated the observed GPP to contrasting peatland hydrological conditions under similar meteorological forcing. We illustrate the ability of the DIMONA platform to facilitate the parameterization of DIMONA models for any geographic location, as well as its ability to perform IoT modelling of real-time photosynthesis at two site locations and ML modelling for ecosystem GPP as a complementary tool to PB modelling.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".