Influence of Swiss needle cast on foliage age-class structure and vertical foliage distribution in Douglas-fir plantations in north coastal Oregon
Bibliographic record
Abstract
Swiss needle cast (SNC) causes premature loss of foliage and subsequent growth decline in Douglas-fir (Pseudotsuga menziesii (Mirb.) Franco). Although the mechanisms leading to this growth decline include loss of photosynthetic surface area and physiological disruption of surviving foliage, estimating the relative contribution of these two primary sources requires precise quantification of SNC effects on total foliage mass, foliage age-class structure, and vertical foliage distribution. The effect of SNC severity on these crown structural attributes was tested across a range of stand densities and site qualities in 10- to 60-year-old plantations in north coastal Oregon. Foliage mass in each age-class was sampled at the branch level, and the resulting equations were applied to all live branches on intensively measured sample trees. Vertical distribution of each foliage age-class was described by a beta distribution fitted to each sample tree, and sources of variation in vertical distribution were tested by regressing beta parameter estimates on SNC intensity and other covariates representing tree, stand, and site attributes. Distribution of foliage mass by age-class and by relative height in the crown was significantly affected by SNC severity, in addition to other covariates such as crown size and tree social position. SNC caused a reduction in the amount of foliage in each age-class and greater relative representation of younger needles. SNC also shifted the mode of relative vertical distribution toward the top of the tree for the three youngest foliage age-classes, but toward the base of the crown for 4- and 5-year-old foliage. Quantification of foliage age-class structure and vertical distribution across a range of SNC severity has helped to establish diagnostic criteria for assessing changes in crown structure that precede declines in growth and vigor. The induced changes in crown structure will also help to identify the relative contribution of several mechanisms causing growth losses in diseased trees.
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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".