Modeling Global and Regional Net Primary Production under Elevated Atmospheric CO2: On a Potential Source of Uncertainty
Bibliographic record
Abstract
Abstract Terrestrial ecosystem models are built, among several reasons, to explore how the Earth’s biosphere responds to climate change and to the projected continual increase of atmospheric CO2 concentration. Many of these models adopt the Farquhar et al. approach, in which leaf carbon assimilation of C3 plants is regulated by two limitations depending on the rate of Rubisco activity and ribulose-1, 5-bisphosphate regeneration (RuBP). This approach was expanded upon by others to include a third limitation that expresses the occurrence, in some plant species, of a photosynthetic downregulation under high concentrations of ambient CO2. Several ecosystem models, however, constrain leaf photosynthesis using only two limitations according to the original formulation of Farquhar et al. and thus neglect the limitation that represents the downregulation of photosynthesis under elevated atmospheric CO2. In this study, the authors first reviewed the effect of elevated CO2 on photosynthesis of C3 plants, which illustrated that short-term observations are likely to considerably underestimate the number of plant species that exhibit a photosynthetic downregulation. Several recent long-term field observations have shown that such downregulation starts to be effective only after several seasons/years of plant exposure to elevated CO2. Second, an ecosystem model was used to illustrate that neglecting the photosynthetic downregulation may significantly bias predictions of net primary production of the middle and high latitudes under high atmospheric CO2 concentrations. Based on both review of field observations and results of simulations, the authors conclude that a more appropriate representation of plant physiology and choice of plant functional types may be required in ecosystem models in order to accurately simulate plant responses to changing environmental conditions.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.001 | 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".