Responses of respiration in the light to warming in field‐grown trees: a comparison of the thermal sensitivity of the Kok and Laisk methods
Bibliographic record
Abstract
Summary The Kok and Laisk techniques can both be used to estimate light respiration R light . We investigated whether responses of R light to short‐ and long‐term changes in leaf temperature depend on the technique used to estimate R light . We grew Eucalyptus tereticornis in whole‐tree chambers under ambient temperature (AT) or AT + 3°C (elevated temperature, ET). We assessed dark respiration R dark and light respiration with the Kok ( R Kok ) and Laisk ( R Laisk ) methods at four temperatures to determine the degree of light suppression of respiration using both methods in AT and ET trees. The ET treatment had little impact on R dark , R Kok or R Laisk . Although the thermal sensitivities of R Kok or R Laisk were similar, R Kok was higher than R Laisk . We found negative values of R Laisk at the lowest measurement temperatures, indicating positive net CO 2 uptake, which we propose may be related to phosphoenolpyruvate carboxylase activity. Light suppression of R dark decreased with increasing leaf temperature, but the degree of suppression depended on the method used. The Kok and Laisk methods do not generate the same estimates of R light or light suppression of R dark between 20 and 35°C. Negative rates of R Laisk imply that this method may become less reliable at low temperatures.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".