Relationship Between Leaf Maximum Carboxylation Rate and Chlorophyll Content Preserved Across 13 Species
Bibliographic record
Abstract
Abstract The leaf maximum carboxylation rate ( V cmax ) is a crucial parameter in determining the photosynthetic capacity of plants. Providing accurate estimates of leaf V cmax , that cover large geographic areas and incorporate plant seasonality is central to correctly predicting carbon fluxes within the terrestrial global carbon cycle. Chlorophyll, as the main photon‐harvesting molecule in leaves, is closely linked to plant photosynthesis. However, how the nature of the relationship between the leaf maximum carboxylation rate (scaled to 25°C; V cmax,25 ) and leaf chlorophyll content varies according to plant type is uncertain. In this study, we investigate whether a universal and stable relationship exists between leaf V cmax,25 and leaf chlorophyll content across different plant types and verify it using field experiments. Measurements of leaf chlorophyll content (Chl) and CO 2 response curves were made on 283 crop, shrub, tree, and vegetable leaves, across 13 species, in China and southern Ontario, Canada. A strong relationship was found between the leaf V cmax,25 and chlorophyll content across different plant types ( R 2 = 0.65, p < 0.001). Cross‐validation showed that the model performs well, producing an RMSE value of 15.4 μmol m −2 s −1 . The results confirm that leaf chlorophyll content can be a reliable proxy for estimating V cmax,25 , opening the door to accurate, spatially continuous estimates of V cmax,25 at the global scale.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 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".