Phenolic compounds in Scots pine heartwood: are kelo trees a unique woody substrate?
Bibliographic record
Abstract
Deadwood quality can be a highly significant factor in determining the occurrence of deadwood-dependent organisms such as saproxylic fungi. Rare deadwood substrates that are produced only after a lengthy senescence such as kelo trees may have unique deadwood qualities. Using high-performance liquid chromatography, we compared the phenolic composition of six types of Scots pine (Pinus sylvestris L.) substrates: living mature trees with no fungal sporocarps, living mature trees with Phellinus pini sporocarps, fallen non-kelo trees, soon-to-be kelo (standing), standing kelo, and fallen kelo. The fungal-infected living trees and fallen kelos were found to have more similarities in their phenolic composition when compared with the living and fallen trees and the standing kelos, which gets further pronounced with increasing decay. The results also highlight the uniqueness of the fungal-infected living trees and the fallen kelos and illustrate a possible correlation between fungal infection and the heartwood phenolic composition of Scots pine. However, it remains unclear to what extent the differences in phenolic compositions are caused by fungal infection and fungal decomposition. We also observed a previously undocumented correlation between the phenolic groups and fire scars on the trunks of the trees. The variation in substrate quality warrants further consideration when deadwood restoration activities are planned, as the quality of the deadwood could be as equally important as the quantity.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".