Global convergence in leaf respiration from estimates of thermal acclimation across time and space
Bibliographic record
Abstract
Recent compilations of experimental and observational data have documented global temperature-dependent patterns of variation in leaf dark respiration (R), but it remains unclear whether local adjustments in respiration over time (through thermal acclimation) are consistent with the patterns in R found across geographical temperature gradients. We integrated results from two global empirical syntheses into a simple temperature-dependent respiration framework to compare the measured effects of respiration acclimation-over-time and variation-across-space to one another, and to a null model in which acclimation is ignored. Using these models, we projected the influence of thermal acclimation on: seasonal variation in R; spatial variation in mean annual R across a global temperature gradient; and future increases in R under climate change. The measured strength of acclimation-over-time produces differences in annual R across spatial temperature gradients that agree well with global variation-across-space. Our models further project that acclimation effects could potentially halve increases in R (compared with the null model) as the climate warms over the 21st Century. Convergence in global temperature-dependent patterns of R indicates that physiological adjustments arising from thermal acclimation are capable of explaining observed variation in leaf respiration at ambient growth temperatures across the globe.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".