<b>Effects of density control and fertilization on growth and yield of young Douglas-fir plantations in the Pacific Northwest</b>
Bibliographic record
Abstract
To examine the effects of density control and fertilization on stand growth and yield of young Douglas-fir ( Pseudotsuga menziesii (Mirb.) Franco) plantations, seven treatment regimes were applied in sixty-three 9-year-old plots from nine installations across western Washington and Oregon. Fertilizer was applied at the rate of 220 kg N·ha–1 (as urea) at stand establishment and every 4 years thereafter. Results after 12 years showed that widely spaced stands exhibited significantly larger quadratic mean diameter than did narrowly spaced stands. The densest stands initially had the greatest overall stand basal area and volume, but accumulation rate in the dense stands had been declining with time. After 12 years, the less dense stands had met or exceeded the basal area periodic annual increment of dense stands. Across all densities, the periodic annual increments of quadratic mean diameter, basal area, and volume in fertilized plots were significantly greater than in unfertilized plots following the first and second urea applications. However, the first fertilization was insufficient to produce a significant increase in stand yield and the significant fertilization increases in yield were found following the second and the third urea applications. This study showed neither significant fertilization effect nor density effect on dominant height. In addition, no significant interactions were found for any stand growth and yield variables considered, but fertilization responses showed different trends among density treatments over time.
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 | 0.001 |
| 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) | 0.000 | 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".