Site temperatures influence seasonal changes in terpene composition in Douglas-fir vegetative buds and current-year foliage
Bibliographic record
Abstract
Over a 3-year period (19982000), variations in terpene composition was measured in vegetative buds of Douglas-fir (Pseudotsuga menziesii (Mirb.) Franco) from six sites varying in elevation and geographic location with sampling from early spring to midsummer. Eleven terpenes were found in virtually all samples (tricyclene, α-pinene, camphene, sabinene, β-pinene, myrcene, Δ-3-carene, limonene, β-phellandrene, terpinolene, and bornyl acetate) and represented an average of 87% of the total terpenes. In each year, composition of the terpene mix varied significantly (P [Formula: see text] 0.05) for all sites and dates, with some significant site and date interactions. Degree-day accumulations were calculated for all sites and years. Patterns of change in terpene composition between sites, areas, and years were strongly related to the temperature regimes associated with site and year.
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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) | 0.001 | 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".