Impact of the foliar pathogen Swiss needle cast on wood quality of Douglas-fir
Bibliographic record
Abstract
Many stands of Douglas-fir (Pseudotsuga menziesii (Mirb.) Franco) near coastal areas of Oregon and Washington are heavily infected with the foliar pathogen causing Swiss needle cast (SNC) disease, and yet there is very little research on the resulting wood quality. Modulus of elasticity (MOE), modulus of rupture (MOR), microfibril angle (MFA), wood density, latewood proportion, and sapwood moisture content were examined in 20- to 28-year-old trees from 15 stands that were infected with varying intensities of SNC. SNC severity was quantified by measuring needle retention, the number of needle cohorts retained at three crown levels. Correlations between disease severity and wood properties were examined at both the stand and within-stand levels. Trees from heavily infected stands (needle retention <2 years) had higher MOE, wood density, and latewood proportion and lower sapwood moisture content than trees from healthier stands. Breast-height age (BHage) was also correlated with these properties, but age alone did not explain all of the increases. MFA was not associated with SNC severity. Within stands, needle retention was not associated with MOE or MOR. The increase in latewood proportion in diseased stands appears to be the driving factor behind their increase in stiffness (MOE). Ring width decreased with decreased needle retention, and the examined wood properties generally showed stronger correlations with ring width than with needle retention.
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 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".