Laisk measurements in the nonsteady state: Tests in plants exposed to warming and variable CO2 concentrations
Bibliographic record
Abstract
Light respiration (RL) is an important component of plant carbon balance and a key parameter in photosynthesis models. RL is often measured using the Laisk method, a gas exchange technique that is traditionally employed under steady-state conditions. However, a nonsteady-state dynamic assimilation technique (DAT) may allow for more rapid Laisk measurements. In 2 studies, we examined the efficacy of DAT for estimating RL and the parameter Ci* (the intercellular CO2 concentration where Rubisco's oxygenation velocity is twice its carboxylation velocity), which is also derived from the Laisk technique. In the first study, we compared DAT and steady-state RL and Ci* estimates in paper birch (Betula papyrifera) growing under control and elevated temperature and CO2 concentrations. In the second, we compared DAT-estimated RL and Ci* in hybrid poplar (Populus nigra L. × P. maximowiczii A. Henry "NM6") exposed to high or low CO2 concentration pre-treatments. The DAT and steady-state methods provided similar RL estimates in B. papyrifera, and we found little acclimation of RL to temperature or CO2; however, Ci* was higher when measured with DAT compared to steady-state methods. These Ci* differences were amplified by the high or low CO2 pre-treatments. We propose that changes in the export of glycine from photorespiration may explain these apparent differences in Ci*.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".