Ecophysiological modeling of the climate imprint on photosynthesis and carbon allocation to the tree stem in the North American boreal forest
Bibliographic record
Abstract
Abstract. A better understanding of the coupling between photosynthesis and carbon allocation in the boreal forest, with implicated environmental factors and mechanistic rules, is crucial to accurately predict boreal forest carbon stocks and fluxes, which are significant components of the global carbon budget. Here we adapted the MAIDEN ecophysiological forest model to better consider important processes for boreal tree species, such as non-linear acclimation of photosynthesis to temperature changes, canopy development as a function of previous year climate variables influencing bud formation, and temperature dependence of carbon partition in summer. We tested these modifications in the eastern Canadian taiga using black spruce (Picea mariana (Mill.) B.S.P.) gross primary production and ring-width data. MAIDEN explains 90 % of the observed daily gross primary production variability, 73 % of the full spectrum of the annual ring width variability and 20–30 % of its high frequency component. The positive effect on stem growth due to climate warming in the last decades is well captured by the model. In addition, we illustrate the improvement achieved with each introduced model adaptation and compare the model results with those of linear response functions. This shows that MAIDEN simulates robust relationships with the most important climate variables (those detected by classical response-function analysis), and is a powerful tool for understanding how environmental factors interact with black spruce ecophysiology to influence present-day and future boreal forest carbon fluxes.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".