Dynamic biotic controls of leaf thermoregulation across the diel timescale
Bibliographic record
Abstract
Leaf thermoregulation and consequent leaf-to-air temperature difference (Δ T ) are tightly linked to plant metabolic rates and health. Current knowledge mainly focus on the regulation of environmental conditions on Δ T , while an accurate assessment of biotic regulations with field data remains lacking. Here, we used a trait-based model that integrates a coupled photosynthesis-stomatal conductance model with a leaf energy balance model to explore how six leaf traits (i.e. leaf width, emissivity, visible and near-infrared light absorptance, photosynthetic capacity— V c,max25 , and stomatal slope— g 1 ) regulate Δ T variability across the diel timescale. We evaluated the model with field observations collected from temperate to tropical forests. Our results show that: (1) leaf traits mediate large Δ T variability, with the noon-time trait-mediated Δ T variability reaching c. 15.0 °C; (2) leaf width, V c,max25 , and g 1 are the three most important traits and their relative importance in Δ T regulation varies strongly across the diel timescale; and (3) model-derived trait-Δ T relationships match field observations that were collected close to either midday or midnight. These findings advance our understanding of biotic controls of leaf-level Δ T variability, highlighting a trait-based representation of leaf energy balance that can improve simulations of diverse leaf thermoregulation strategies across species and physiological responses to climate change.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".