Seasonal nitrogen cycling in the bark of field-grown Grey poplar is correlated with meteorological factors and gene expression of bark storage proteins
Bibliographic record
Abstract
Seasonal tree-internal nitrogen cycling is an important strategy for trees to achieve high efficiency in the use of nitrogen (N). Key processes of this N redistribution are autumnal leaf senescence and storage of released N as bark storage proteins (BSP) in perennial tissues. While the regulation of leaf senescence has been intensively analysed in trees, the coordination of the complementary storage processes is still poorly understood. Therefore, we ascertained relationships between physiological-level and molecular-level processes and environmental factors under natural conditions in the bark of Populus x canescens. We analysed amino-N concentrations, total soluble protein concentration and transcript abundances of BSP genes in the bark of field-grown P. x canescens harvested during two annual growth cycles. By correlation analysis and linear modelling, we assessed interactions between biological data and meteorological conditions. Day length correlated with BSP expression, and air temperature correlated strongly with total protein concentration (r = -0.92), gamma-aminobutyric acid (GABA; r = 0.76) and arginine (r = -0.70). GABA and arginine also correlated significantly with total protein concentration and transcript abundances of BSP genes. We conclude that GABA and arginine potentially contribute to adjust storage processes in the bark of poplar trees to seasonal changes in environmental conditions.
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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".