Changes in cortical and wood terpenes in Sitka spruce in response to wounding
Bibliographic record
Abstract
Terpene levels were measured in bark and wood samples of Sitka spruce (Picea sitchensis (Bong.) Carrière) trees. The trees that had been selected as early or late flushing were subjected to artificial wounding to simulate attack by the white pine weevil, Pissodes strobi (Peck). Samples were taken at four times during the growing season: before wounding, shortly after wounding, and two times later in the season. Terpenes were extracted with hexane and quantified by capillary gas chromatography. There were no significant differences in total bark terpenes between early- and late-flushing trees or between control and wounded trees over all sampling times. There were no significant differences in total wood terpenes between early- and late-flushing trees or among sampling times, but a statistically significant difference was found between control and wounded trees. Eleven individual terpenes accounted for the majority of the terpenes in the extracts. Four bark terpenes showed significant differences with sampling time, two with flushing class, and none with treatment. Five wood terpenes showed significant differences with sampling time, two with flushing class, and nine with treatment. We concluded that flushing had only a minor effect on bark and wood terpene profiles and that wounding significantly increased terpene concentration in wood shortly after injury.
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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 | 0.001 | 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".