Modeling Peat Thermal Regime of an Ombrotrophic Peatland with Hummock–Hollow Microtopography
Bibliographic record
Abstract
The theory of conductive heat transfer cannot explain different attenuations of the daily amplitude of peat temperatures ( T S ) in hummocks (detectable below the 20‐cm depth) and hollows (disappearing above the 10‐cm depth). Large readily drained macropores in the upper fibric peat determine a large air permeability and hence may enhance heat transfer by air convection in porous media, driven by temperature gradients between hummock sides and interiors. In this study, the ecosys model was used to simulate a peat thermal regime at Mer Bleue peatland, Ontario, Canada. It was hypothesized that adding the air‐convective heat transfer to conductive plus water‐convective heat transfers would improve simulations of T S The results for T S , ground heat fluxes, G , and sensible heat fluxes, H , modeled with and without air‐convective heat transfer were tested with continuous hourly measurements from 2000 to 2004 using thermocouples, heat flux plates, and eddy covariance. Simulated air‐convective heat transfer caused an average increase in G and a corresponding decrease in H of ∼20 W m −2 from the simulated conductive plus water‐convective heat transfer. Hastened soil warming in hummocks resulted in better agreement between measured and simulated hummock T S values with (RMSD of 2.23°C) than without air‐convective heat (RMSD of 2.54°C). Enhanced hummock T S caused an indirect increase in hollow T S in the model with (RMSD of 1.68°C) compared to without air‐convective heat (RMSD of 1.82°C). Our results suggest that air convection is probably an important mechanism of heat transfer in peat hummocks and should be included in peatland biogeochemical models.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".