Terpenoids in the wood of Scots pine and Norway spruce seedlings exposed to ozone at different nitrogen availability
Bibliographic record
Abstract
Scots pine (Pinus sylvestris L.) and Norway spruce (Picea abies (L.) Karst.) seedlings were exposed to low-level ozone (O3) in an open-air exposure system for three or two growing seasons, respectively. Simultaneously, seedlings were supplied with nutrient solution containing either optimum (control) nitrogen (N), 70% of optimum N (low N availability), or 150% of optimum N (high N availability). Carbon-based secondary compounds, e.g., terpenes and resin acids, were analysed from the stemwood. α-Pinene, 3-carene, and limonene + β-phellandrene were the major terpenes in pine wood, whereas α-pinene, β-pinene, and limonene + β-phellandrene dominated in the spruce wood. Palustric + levopimaric and abietic acids were the major resin acids in the wood of both species. Ozone exposure did not cause any remarkable changes in the concentrations and proportional quantities of terpenes and resin acids in the wood of either tree species. Nitrogen availability did not affect the concentrations of carbon-based secondary compounds either, and the effects on proportional quantities were slight and ambiguous with respect to N supply. We conclude that realistically elevated levels of O3 do not have effects on concentrations of carbon-based secondary compounds of conifer wood and N availability might cause only slight changes in them. Therefore, our results do not directly support the carbonnutrient balance hypothesis.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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".