Responses of needle terpene concentrations and characteristics of resin canals to different warming treatments in Scots pine and Norway spruce seedlings grown in a field experiment
Bibliographic record
Abstract
We studied responses of needle terpene concentrations and resin canal characteristics to warming in Scots pine ( Pinus sylvestris) and Norway spruce ( Picea abies) seedlings grown in a controlled field set-up in eastern Finland. Warming was simulated during the growing seasons using infrared heaters that increased air temperature in study 1 (2016–2019) by 1 °C and in study 2 (2019–2020) by 2 and 4 °C, compared to ambient conditions. Terpenes were sampled in study 1 from non-matured current year and matured previous year needles in June 2019, and study 2 from mature current year needles in August 2020. In study 1, we also studied resin canal anatomy. We found that 1 °C elevation of temperature caused two-fold increase in concentrations of total terpenes, oxygenated monoterpenes, and sesquiterpenes of non-matured current year needles of Norway spruce. Further, it caused 1.3–1.8-fold increases in sesquiterpene concentrations both in unmatured and matured needles of Scots pine. It also decreased resin canal diameter of mature needles in Norway spruce. In study 2, the stronger warming treatments did not affect terpene concentrations of matured current year needles in either species. Based on our findings, even minor elevation of temperature may affect terpene concentrations of non-mature needles in boreal conifers.
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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 | 0.001 |
| 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".