Histology and cell wall biochemistry of stone cells in the physical defence of conifers against insects
Bibliographic record
Abstract
Conifers possess an array of physical and chemical defences against stem-boring insects. Stone cells provide a physical defence associated with resistance against bark beetles and weevils. In Sitka spruce (Picea sitchensis), abundance of stone cells in the cortex of apical shoots is positively correlated with resistance to white pine weevil (Pissodes strobi). We identified histological, biochemical and molecular differences in the stone cell phenotype of weevil resistant (R) or susceptible (S) Sitka spruce genotypes. R trees displayed significantly higher quantities of cortical stone cells near the apical shoot node, the primary site for weevil feeding. Lignin, cellulose, xylan and mannan were the most abundant components of stone cell secondary walls, respectively. Lignin composition of stone cells isolated from R trees contained a higher percentage of G-lignin compared with S trees. Transcript profiling revealed higher transcript abundance in the R genotype of coumarate 3-hydroxylase, a key monolignol biosynthetic gene. Developing stone cells in current year apical shoots incorporated fluorescent-tagged monolignol into the secondary cell wall, while mature stone cells of previous year apical shoots did not. Stone cell development is an ephemeral process, and fortification of shoot tips in R trees is an effective strategy against insect feeding.
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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".