Long-term effect of elevated air humidity on seasonal variability in diurnal leaf conductance and gas exchange in silver birch
Bibliographic record
Abstract
Environmental conditions and photoperiod length drive the seasonal variability of gas exchange in plants. Still, little is known about trees’ adaptation to climate change, expressed as a delay in decreasing photosynthetic capacity at the end of the growing season. We investigated the effect of elevated air humidity (RH) and sampling period (from July to September) on the variability of net photosynthesis (An), dark respiration (R), daytime (gl_day) and night-time (gl_night) leaf conductance, an index of leaf chlorophyll content (SPAD), and An:SPAD ratio in cut shoots of silver birch (Betula pendula Roth). Measurements of cut shoots were conducted in a climate chamber to eliminate the direct effect of field conditions. The An, An:SPAD ratio, and gl_day were higher in the humidification treatment (H) than in the control (C) (P < 0.05) in autumn. The R was higher in the control than in the humidification treatment across the study period. The gl_night increased considerably in September in both treatments (P < 0.05) and was significantly correlated with R. Our findings suggest that autumnal leaf conductance and gas exchange in silver birch are considerably influenced by long-term exposure to elevated RH and are probably related to a complex of leaf senescence processes, including nitrogen retranslocation.
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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".