Accurate photosynthetic parameter estimation at low stomatal conductance: effects of cuticular conductance and instrumental noise
Bibliographic record
Abstract
Abstract Accurate estimation of photosynthetic parameters is essential for understanding plant physiological limitations and responses to environmental factors from the leaf to the global scale. Gas exchange is a useful tool to measure responses of net CO2 assimilation (A) to internal CO2 concentration (Ci), a necessary step in estimating photosynthetic parameters including the maximum rate of carboxylation (Vcmax) and the electron transport rate (Jmax). However, species and environmental conditions of low stomatal conductance (gsw) reduce the signal-to-noise ratio of gas exchange, challenging estimations of Ci. Previous works showed that not considering cuticular conductance to water (gcw) can lead to significant errors in estimating Ci, because it has a different effect on total conductance to CO2 (gtc) than does gsw. Here we present a systematic assessment of the need for incorporating gcw into Ci estimates. In this study we modeled the effect of gcw and of instrumental noise and quantified these effects on photosynthetic parameters in the cases of four species with varying gsw and gcw, measured using steady-state and constant ramping techniques, like the rapid A/Ci response method. We show that not accounting for gcw quantitatively influences Ci and the resulting Vcmax and Jmax, particularly when gcw exceeds 7% of the total conductance to water. The influence of gcw was not limited to low gsw species, highlighting the importance of species-specific knowledge before assessing A/Ci curves. Furthermore, at low gsw instrumental noise can affect Ci estimation, but the effect of instrumental noise can be minimized using constant-ramping rather than steady-state techniques. By incorporating these considerations, more precise measurements and interpretations of photosynthetic parameters can be obtained in a broader range of species and environmental conditions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.000 | 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".