Environmental factors have a greater influence on photosynthetic capacity in <scp>C<sub>4</sub></scp> plants than biochemical subtypes or growth forms
Bibliographic record
Abstract
Summary Our understanding of how photosynthetic capacity varies among C4 species and across growth and measurement conditions remains limited. We collated 1696 CO2 response curves of net CO2 assimilation rate (A/Ci curves) from C4 species grown and measured at various environmental conditions and used these data to estimate the apparent maximum carboxylation activity of phosphoenolpyruvate carboxylase (VpmaxA) and CO2‐saturated net photosynthetic rate (Amax), two key parameters describing photosynthetic capacity. We examined how VpmaxA and Amax vary with species‐specific traits, growth and measurement conditions. We found little systematic variation of VpmaxA and Amax across the classical C4 biochemical subtypes or growth forms, but showed that growth temperature and measurement conditions are major factors determining C4 photosynthetic capacity. We found no evidence that common C4 model species (e.g. maize, sorghum and Setaria viridis) differ in photosynthetic capacity from other C4 species when grown in controlled environments. However, C4 model species showed up to twice the photosynthetic capacity of other C4 species when grown in the field. Our multivariate model accounts for 47–51% of the variation reported in VpmaxA and Amax, and we argue that environmental conditions have a greater influence on C4 photosynthetic capacity than biochemical subtypes or growth forms.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".