Western redcedar response to precommercial thinning and fertilization through 25 years posttreatment
Bibliographic record
Abstract
There is little information available on the long-term effects of managing western redcedar ( Thuja plicata Donn ex D. Don). In a 15- to 20-year-old naturally regenerated second-growth redcedar stand on a poor site on the Olympic Peninsula of Washington, we tested crop tree (largest 250 trees·ha–1) response to precommercial thinning and fertilization in a replicated study. Fertilization treatments were N or N+P applied at study installation and year 13; precommercial thinning occurred at installation. Precommercial thinning without fertilization produced a sustained increase in periodic individual-tree basal area (BA) growth rate from years 3 through 25 posttreatment. However, through year 12, higher BA growth rates resulted from fertilization. During years 13 through 25, when intraspecific competition increased, the highest BA growth rate resulted from the combination of fertilization and precommercial thinning. Compared with the unthinned–unfertilized control, fertilization without thinning increased year-25 crop-tree height by 34% and BA by 137%; thinning without fertilization increased height by 11% and BA by 91%. Height to live-crown base was decreased by thinning but increased by fertilization, while thinning significantly increased stem taper on the lower bole. Treatment responses and foliar analyses indicate crop tree growth was substantially limited by nutrient availability.
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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".