Effects of radial growth rate on outerwood properties of coastal Douglas-fir in healthy stands versus stands impacted by Swiss needle cast
Bibliographic record
Abstract
A common belief in forestry is that rapid growth in coastal Douglas-fir (Pseudotsuga menziesii (Mirb.) Franco var. menziesii) results in decreased outerwood quality. In Oregon, the opposite pattern has been reported for stands affected by Swiss needle cast, whereby a native fungus causes premature needle drop and an increase in latewood proportion (LW%), wood density, stiffness (MOE), and strength (MOR). Using a combination of new and existing data, we compared the properties of ∼25-year-old outerwood from 18 healthy and 14 diseased stands using direct tests (6–8 beams from 7–12 trees per stand, 2614 beams total) and indirect SilviScanII tests (1 sample for each of the 366 trees). As seen before, diseased stands showed a decrease in wood quality with growth rate: ring count was strongly and positively correlated with density, MOE, and MOR (r2 = 0.74, 0.65, and 0.63), and LW% was positively correlated with ring count, density, MOE, and MOR (r2 = 0.50, 0.62, 0.30, and 0.44). In contrast, healthy stands showed no significant effect of ring count on density, MOE, or MOR. LW% was weakly and significantly correlated with MOE (r2 = 0.25) but not with ring count, density, or MOR. Among healthy stands, growth acceleration had no adverse effects on outerwood properties.
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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 | 0.001 |
| 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".