Resin vesicles in conifer seeds: morphology and allelopathic effects
Bibliographic record
Abstract
The seed coat of fir (Abies), hemlock (Tsuga), and cedar (Thuja) species contain terpenoid resin vesicles. Although information is limited about the morphology and allelopathy of these vesicles, their damage during seed processing can negatively impact germination success. We examined resin vesicle morphology of western redcedar (Thuja plicata Donn ex D. Don), eastern white cedar (Thuja occidentalis L.), amabilis fir (Abies amabilis Douglas ex J. Forbes), balsam fir (Abies balsamea (L.) Mill.), grand fir (Abies grandis (Douglas ex D. Don) Lindl), and subalpine fir (Abies lasiocarpa (Hook.) Nutt.) seeds by 1 H magnetic resonance imaging to characterize resin vesicle volume, shape, and number. Western redcedar genotypes with known differences in the quantity of foliar monoterpenes also had parallel differences in the resin vesicle volume of corresponding seeds. Germination assays with the cedar and fir species, eastern hemlock (Tsuga canadensis (L.) Carrière), mountain hemlock (Tsuga mertensiana (Bong.) Carrière), and western hemlock (Tsuga heterophylla (Raf.) Sarg.) confirmed that resin vesicle damage prior to stratification (moist chilling) significantly reduced germination success for most species. Extracts of these resin vesicles from the Abies and Thuja species strongly inhibited the germination of Arabidopsis Col-0 seeds but inhibited the germination of only a small percentage of Arabidopsis abscisic acid insensitive mutant abi3-6 seeds. Resin extracts from Thuja species were 10 times more effective than those from Abies species in inhibiting Arabidopsis Col-0 germination.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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) | 0.000 | 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 teacher head, 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".