Peatland vegetation composition and phenology drive the seasonal trajectory of maximum gross primary production
Bibliographic record
Abstract
Abstract Gross primary production (GPP) is a key driver of the peatland carbon cycle. Although many studies have explored the apparent GPP under natural light conditions, knowledge of the maximum GPP at light-saturation (GPP max ) and its spatio-temporal variation is limited. This information, however, is crucial since GPP max essentially constrains the upper boundary for apparent GPP. Using chamber measurements combined with an external light source across experimental plots where vegetation composition was altered through long-term (20-year) nitrogen addition and artificial warming, we could quantify GPP max in-situ and disentangle its biotic and abiotic controls in a boreal peatland. We found large spatial and temporal variations in the magnitudes of GPP max which were related to vegetation species composition and phenology rather than abiotic factors. Specifically, we identified vegetation phenology as the main driver of the seasonal GPP max trajectory. Abiotic anomalies (i.e. in air temperature and water table level), however, caused species-specific divergence between the trajectories of GPP max and plant development. Our study demonstrates that photosynthetically active biomass constrains the potential peatland photosynthesis while abiotic factors act as secondary modifiers. This further calls for a better representation of species-specific vegetation phenology in process-based peatland models to improve predictions of global change impacts on the peatland carbon cycle.
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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) | 0.001 | 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".