Integrating peatlands and permafrost into a dynamic global vegetation model: 2. Evaluation and sensitivity of vegetation and carbon cycle processes
Bibliographic record
Abstract
Peatlands and permafrost are important components of the carbon cycle in the northern high latitudes. The inclusion of these components into a dynamic global vegetation model required changes to physical land surface routines, the addition of two new peatland‐specific plant functional types, incorporation of an inundation stress mechanism, and deceleration of decomposition under inundation. The new model, LPJ‐WHy v1.2, was used to simulate net ecosystem production (NEP), net primary production (NPP), heterotrophic respiration (HR), and soil carbon content. Annual peatland NEP matches observations even though the seasonal amplitude is overestimated. This overestimation is caused by excessive NPP values, probably due to the lack of nitrogen or phosphorus limitation in LPJ‐WHy. Introduction of permafrost reduces circumpolar (45–90°N) NEP from 1.65 to 0.96 Pg C a −1 and leads to an increase in soil carbon content of almost 40 Pg C; adding peatlands doubles this soil carbon increase. Peatland soil carbon content and hence HR depend on model spin‐up duration and are crucial for simulating NEP. These results highlight the need for a regional peatland age map to help determine spin‐up times. A sensitivity experiment revealed that under future climate conditions, NPP may rise more rapidly than HR resulting in increases in NEP.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".