Can leaf net photosynthesis acclimate to rising and more variable temperatures?
Bibliographic record
Abstract
Abstract Under future climates, leaf temperature (Tl) will be higher and more variable. This will affect plant carbon (C) balance because photosynthesis and respiration both respond to short‐term (subdaily) fluctuations in Tl and acclimate in the longer term (days to months). This study asks the question: To what extent can the potential and speed of photosynthetic acclimation buffer leaf C gain from rising and increasing variable Tl? We quantified how increases in the mean and variability of growth temperature affect leaf performance (mean net CO2 assimilation rates, Anet; its variability; and time under near‐optimal photosynthetic conditions), as mediated by thermal acclimation. To this aim, the probability distribution of Anet was obtained by combining a probabilistic description of short‐ and long‐term changes in Tl with data on Anet responses to these changes, encompassing 75 genera and 111 species, including both C3 and C4 species. Our results show that (a) expected increases in Tl variability will decrease mean Anet and increase its variability, whereas the effects of higher mean Tl depend on species and initial Tl, and (b) acclimation reduces the effects of leaf warming, maintaining Anet at >80% of its maximum under most thermal regimes.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| 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".