Variation in Douglas-fir (<i>Pseudotsuga menziesii</i> var. <i>menziesii</i>) and red alder (<i>Alnus rubra</i>) stem taper across varying stand conditions in the Pacific Northwest
Bibliographic record
Abstract
Tree taper has been of interest for over a century, yet questions remain regarding the effects of silvicultural treatments and forest health on recoverable volume. This work utilizes data from Douglas-fir ( Pseudotsuga menziesii (Mirb.)) ( n = 608) and red alder ( Alnus rubra (Bong.)) ( n = 495) trees to assess the influences of fertilization, pruning, thinning, regeneration origin, and defoliation caused by Swiss Needle Cast (SNC; Nothophaeocryptopus gaeumannii), on stem taper in the Pacific Northwest. The Kozak (2004; For. Chor. 80: 507–515) variable-exponent equation was used to test the addition of treatment and crown variables as the model is widely regarded for its flexibility in application. Using a mixed effects framework, results reveal that thinning of Douglas-fir can result in a 3.5% increase in upper stem diameter inside bark, while pruning may lead to a 4.1% decrease. SNC-induced defoliation of Douglas-fir reduced mean diameter above-breast height by 11.5%. Total volume of artificially regenerated red alder was 16% greater than naturally regenerated stems. Overall, thinning of healthy Douglas-fir and planting red alder may increase recoverable volume and C captured in long-term timber products in the region, and the inclusion of crown variables can increase the predictive power of taper estimates for some species.
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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 | 0.000 |
| 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) | 0.000 | 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".