Functions of stone cells and oleoresin terpenes in the conifer defense syndrome
Bibliographic record
Abstract
Conifers depend on complex defense systems against herbivores. Stone cells (SC) and oleoresin are physical and chemical defenses of Sitka spruce that have been separately studied in previous work. Weevil oviposit at the tip of the previous year's apical shoot (PYAS). We investigated interactions between weevil larvae and trees in controlled oviposition experiments with resistant (R) and susceptible (S) Sitka spruce. R trees have an abundance of SC in the PYAS cortex. SC are mostly absent in S trees. R trees and S trees also differ in the composition of oleoresin terpenes. Transcriptomes of R and S trees revealed differences in long-term weevil-induced responses. Performance of larvae was significantly reduced on R trees compared with S trees under experimental conditions that mimicked natural oviposition behavior at apical shoot tips and may be attributed to the effects of SC. In oviposition experiments designed for larvae to feed below the area of highest SC abundance, larvae showed an unusual feeding behavior and oleoresin appeared to function as the major defense. The results support a role for both SC and oleoresin terpenes and possible synergies between these traits in the defense syndrome of weevil-resistant Sitka spruce.
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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".