Maximum Carboxylation Rate Estimation With Chlorophyll Content as a Proxy of Rubisco Content
Bibliographic record
Abstract
Abstract The maximum carboxylation rate (Vcmax) is a key parameter in determining the plant photosynthesis rate per unit leaf area. However, most terrestrial biosphere models currently treat Vcmax as constants changing only with plant functional types, leading to large uncertainties in modeled carbon fluxes. Vcmax is tightly linked with Ribulose‐1,5‐bisphosphate carboxylase/oxygenase (Rubisco). Here we investigated the relationship between leaf chlorophyll and Rubisco (Chl‐Rub) contents within a winter wheat paddock. With chlorophyll as a proxy of Rubisco, a semimechanistic model was developed to model Vcmax 25 (Vcmax normalized to 25°C) . The Chl‐Rub relationship was validated using measurements in a temperate mixed deciduous forest in Canada. The results showed that Rubisco was strongly correlated with chlorophyll ( R 2 = 0.96, p < 0.001) for winter wheat since the absorption of light energy by chlorophyll and the amount of CO 2 catalyzed by Rubisco are tightly coupled. Incorporating the Chl‐Rub relationship into the semimechanistic model, the root mean square error of modeled Vcmax 25 was the lowest among all estimation models. The slopes of Chl‐Rub relations were almost consistent in the winter wheat and temperate forest, demonstrating the potential for using leaf chlorophyll content as a proxy of leaf Rubisco in modeling Vcmax 25 at large spatial scales. We anticipate that improving Vcmax 25 estimates over time and space will reduce uncertainties in global carbon budgets simulated by terrestrial biosphere models.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".