Rubisco, Rubisco activase, and global climate change
Bibliographic record
Abstract
Global warming and the rise in atmospheric CO(2) will increase the operating temperature of leaves in coming decades, often well above the thermal optimum for photosynthesis. Presently, there is controversy over the limiting processes controlling photosynthesis at elevated temperature. Leading models propose that the reduction in photosynthesis at elevated temperature is a function of either declining capacity of electron transport to regenerate RuBP, or reductions in the capacity of Rubisco activase to maintain Rubisco in an active configuration. Identifying which of these processes is the principal limitation at elevated temperature is complicated because each may be regulated in response to a limitation in the other. Biochemical and gas exchange assessments can disentangle these photosynthetic limitations; however, comprehensive assessments are often difficult and, for many species, virtually impossible. It is proposed that measurement of the initial slope of the CO(2) response of photosynthesis (the A/C(i) response) can be a useful means to screen for Rubisco activase limitations. This is because a reduction in the Rubisco activation state should be most apparent at low CO(2) when Rubisco capacity is generally limiting. In sweet potato, spinach, and tobacco, the initial slope of the A/C(i) response shows no evidence of activase limitations at high temperature, as the slope can be accurately modelled using the kinetic parameters of fully activated Rubisco. In black spruce (Picea mariana), a reduction in the initial slope above 30 degrees C cannot be explained by the known kinetics of fully activated Rubisco, indicating that activase may be limiting at high temperatures. Because black spruce is the dominant species in the boreal forest of North America, Rubisco activase may be an unusually important factor determining the response of the boreal biome to climate change.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".