The importance of species-specific and temperature-sensitive parameterisation of A / C i models: a case study using cotton ( Gossypium hirsutum L.) and the automated ‘OptiFitACi’ R-package.
Bibliographic record
Abstract
Leaf gas exchange measurements provide an important tool for inferring a plant’s photosynthetic biochemistry. In most cases, the responses of photosynthetic CO 2 assimilation to variable intercellular CO 2 concentrations ( A / C i response curves) are used to model the maximum rate of carboxylation by ribulose-1,5-bisphosphate carboxylase/oxygenase (Rubisco, V cmax ) and the rate of electron transport at a given photosynthetically active radiation (PAR; J PAR ). The standard Farquhar-Von Caemmerer-Berry model is typically used with default parameters of Rubisco kinetic values and mesophyll conductance to CO 2 ( g m ) derived from tobacco that impairs analytical reliability across species. To study this, here we measured the temperature responses of key in vitro Rubisco catalytic properties and g m in cotton ( Gossypium hirsutum cv. Sicot 71) and derived V cmax and J 2000 ( J at 2000 µmol m -2 s -1 PAR) from cotton A / C i curves incrementally measured at 15°C to 40°C using cotton and tobacco parameters with our new automated fitting R package ‘OptiFitACi’. When applied to cotton, the tobacco parameters produced unrealistic J 2000 : V cmax ratio of <1 at 25°C, two- to three-fold higher estimates of V cmax , approximately 50% higher estimates of J 2000 and more variable estimates of V cmax and J 2000 , compared to model parameterisation with cotton-derived values. We determined that errors arise when using a g m of 0.23 mol m -2 s -1 bar -1 or below and Rubisco CO 2 -affinities under ambient O 2 ( K C 21%O2 ) outside 461 µbar to 627 µbar to model A / C i responses in cotton. We show how the multi- A / C i modelling capabilities of ‘OptiFitACi’ serves as a robust, user-friendly extension of ‘plantecophys’ by providing simplified temperature-sensitivity and species-specificity parameterisation capabilities to enable higher accuracy estimates of V cmax and J 2000 .
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".