Diurnal and seasonal variation in light and dark respiration in field-grown<i>Eucalyptus pauciflora</i>
Bibliographic record
Abstract
Respiration from vegetation is a substantial part of the global carbon cycle and the responses of plant respiration to daily and seasonal fluctuations in temperature and light must be incorporated in models of terrestrial respiration to accurately predict these CO2 fluxes. We investigated how leaf respiration (R) responded to changes in leaf temperature (T(leaf)) and irradiance in field-grown saplings of an evergreen tree (Eucalyptus pauciflora Sieb. ex Spreng). Seasonal shifts in the thermal sensitivity of leaf R in the dark (R(dark)) and in the light (R(light)) were assessed by allowing T(leaf) to vary over the day in field-grown leaves over a year. The Q10 of R (i.e., the relative increase in R for a 10 °C increase in T(leaf)) was similar for R(light) and R(dark) and had a value of ∼ 2.5; there was little seasonal change in the Q10 of either R(light) or R(dark), indicating that we may be able to use similar functions to model short-term temperature responses of R in the dark and in the light. Overall, rates of R(light) were lower than those of R(dark), and the ratio of R(light)/R(dark) tended to increase with rising T(leaf), such that light suppression of R was reduced at high T(leaf) values, in contrast to earlier work with this species. Our results suggest we cannot assume that R(light)/R(dark) decreases with increasing T(leaf) on daily timescales, and highlights the need for a better mechanistic understanding of what regulates light suppression of R in leaves.
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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 | 0.000 |
| 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".