Douglas-fir foliage retention dynamics across a gradient of Swiss needle cast in coastal Oregon and Washington
Bibliographic record
Abstract
Swiss needle cast (SNC) is an important foliage disease of Douglas-fir (Pseudotsuga menziesii (Mirb.) Franco) caused by the native pathogen Nothophaeocryptopus gaeumannii (T. Rohde) Videira, C. Nakash., U. Braun & Crous, which has been present in epidemic proportions in coastal Douglas-fir forests since the 1990s. Under conducive environmental and stand conditions, the fungal fruiting bodies emerge on young needles, inhibiting gas exchange and causing premature needle casting and subsequent growth losses. Using a new regional plot network, which extends and approximately doubles the area of SNC-susceptible coastal forest sampled, we investigated the distribution of SNC disease indices across the region and throughout individual tree crowns. Foliage retention varied from 1.15 to 3.9 years, and disease severity index (incidence × % occluded stomata) ranged from 0.05% to 52.11%. Foliage retention was positively correlated with distance from the coast and elevation, while foliage retention and disease severity were negatively associated across the study area. Within crowns, disease severity was negatively associated with crown depth, and foliage retention was positively associated with crown depth, regardless of distance from coast. Across the entire study area, foliage retention was found to decrease and disease severity increase with latitude, all else being equal. Tree growth metrics are positively associated with increasing foliage retention, and normal growth occurs greater than ∼3.2 years.
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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) | 0.001 | 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".