The DigiBog peatland development model 2: ecohydrological simulations in 2D
Bibliographic record
Abstract
ABSTRACT In the first of this pair of papers we introduced the conceptual and hydrological basis of the peatland development model—DigiBog. Here we describe the submodels which simulate (i) the production of plant litter, (ii) peat decomposition, and (iii) changes in peat hydraulic conductivity due to decomposition. To illustrate how the model works, DigiBog was applied to three example situations: Bogs 1, 2, and 3. For each, the net rainfall was held constant at 30 cm year −1 and the oxic decomposition parameter kept at 0·015 year −1 . The anoxic decomposition parameter varied from 5 × 10 −6 (Bog 1) to 5 × 10 −4 year −1 (Bog 3). Peatland development was simulated for 5000 years. For Bogs 1 and 2, plausible large peatland domes develop. Despite having a higher anoxic decomposition rate, Bog 2 grew thicker than Bog 1. This apparently counter‐intuitive result is caused by the feedback between hydraulic conductivity and degree of peat decomposition. For both Bogs 1 and 2, DigiBog also simulates transitions from wet to dry states, demarked by sudden switches from poorly decomposed to well‐decomposed peat moving upwards in the peat profile. These regime shifts result from internal peatland dynamics and not from allogenic influences, and challenge the view that peat properties are always a reflection of climate. In Bog 3, a ‘mini‐bog’ developed and persisted near the margin of the peatland; this bog can also be explained in terms of the internal feedbacks within the model. Copyright © 2011 John Wiley & Sons, Ltd.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".