Terpenoid metabolite profiling in Sitka spruce identifies association of dehydroabietic acid, (+)-3-carene, and terpinolene with resistance against white pine weevil
Bibliographic record
Abstract
The white pine weevil ( Pissodes strobi Peck) is an important insect pest in the Pacific Northwest that attacks the apical stem leaders of spruce ( Picea spp.) causing damage to tree form, growth, and stand development. Because of attacks by weevils, Sitka spruce ( P. sitchensis Bong.) is not commonly replanted as a commercial species in coastal British Columbia, despite its economic and ecological importance. In the last decade, the focus of research on Sitka spruce resistance against weevils has moved from silvicultural approaches to breeding for resistance. The British Columbia Ministry of Forests and Range, in collaboration with the Canadian Forest Service, has developed a successful program to screen populations and select tree genotypes for resistance to weevil attack. Part of this effort has been the establishment of clonebanks that contain genotypes from throughout the range of Sitka spruce. For metabolite profiling, using gas chromatography coupled with flame ionization detection or mass spectrometry, we analysed 111 different genotypes to determine the relationship of mono- and di-terpenoid oleoresin compounds with the resistance rating. Dehydroabietic acid, a diterpene, was identified as a strong indicator of resistance. Two monoterpenes, (+)-3-carene and terpinolene, were also associated with resistance in genotypes originating from one of the areas (Haney) in which resistance has been noted.
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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) | 0.000 | 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".