On the need to incorporate sensitivity to CO<sub>2</sub> transfer conductance into the Farquhar–von Caemmerer–Berry leaf photosynthesis model
Bibliographic record
Abstract
ABSTRACT Virtually all current estimates of the maximum carboxylation rate ( V cmax ) of ribulose‐1,5‐bisphosphate carboxylase/oxygenase (Rubisco) and the maximum electron transport rate ( J max ) for C 3 species implicitly assume an infinite CO 2 transfer conductance ( g i ). And yet, most measurements in perennial plant species or in ageing or stressed leaves show that g i imposes a significant limitation on photosynthesis. Herein, we demonstrate that many current parameterizations of the photosynthesis model of Farquhar, von Caemmerer & Berry ( Planta 149, 78–90, 1980 ) based on the leaf intercellular CO 2 concentration ( C i ) are incorrect for leaves where g i limits photosynthesis. We show how conventional A–C i curve (net CO 2 assimilation rate of a leaf – A n – as a function of C i ) fitting methods which rely on a rectangular hyperbola model under the assumption of infinite g i can significantly underestimate V cmax for such leaves. Alternative parameterizations of the conventional method based on a single, apparent Michaelis–Menten constant for CO 2 evaluated at C i [ K m (CO 2 ) i ] used for all C 3 plants are also not acceptable since the relationship between V cmax and g i is not conserved among species. We present an alternative A–C i curve fitting method that accounts for g i through a non‐rectangular hyperbola version of the model of Farquhar et al . (1980 ). Simulated and real examples are used to demonstrate how this new approach eliminates the errors of the conventional A–C i curve fitting method and provides V cmax estimates that are virtually insensitive to g i . Finally, we show how the new A–C i curve fitting method can be used to estimate the value of the kinetic constants of Rubisco in vivo is presented
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".