Monoterpene persistence in the sapwood and heartwood of longleaf pine stumps: assessment of differences in composition and stability under field conditions
Bibliographic record
Abstract
Monoterpenes in exudates, phloem, and sapwood have received considerable attention relative to the active defenses of pine trees. However, little is known about the composition and function of the heartwood monoterpenes. To address this deficiency, monoterpene contents and relative compositions were determined for sapwood and heartwood samples from longleaf pine ( Pinus palustris Mill.) stumps monitored in the field for a 1 year period postharvest. Gas chromatography – mass spectrometry analysis of sapwood and heartwood sample extracts showed the total monoterpene contents for both declined at essentially the same rate. For sapwood, α-pinene continued to comprise about 75% of all compounds detected, while the proportion of β-pinene declined with time (15%–7%). For the heartwood, lower proportions of both α- and β-pinenes (64% and 10%, respectively) were offset by higher proportions of other monoterpenes (e.g., limonene, α-terpineol, borneol). The low proportion of β-pinene in very old turpentine and lightwood stump samples further suggested the lower stability of this particular monoterpene. While it has not been specifically demonstrated that the monoterpenes have an active role in the decay resistance of longleaf pine stump heartwood, these compounds do persist for a very long time and thus are available should they serve this function.
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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".