Long-term effects of thinning and fertilization on growth of red fir in northeastern California
Bibliographic record
Abstract
To determine the impact of fertilization and thinning on growth and development of red fir (Abies magnifica A. Murr.) stands, we established an experiment in a 60-year-old stand using a 2 × 3 factorial design with nitrogen-fertilized and nonfertilized treatments and three stocking levels. Plots were established in 1976 and were measured every 5 years for 26 years. The periodic annual increment in basal area was 97%, 51%, 38%, and 33% greater in fertilized trees than in nonfertilized trees during the first, second, third, and fourth 5-year periods, respectively. After 20 years, annual basal area increment was greater in nonfertilized trees. The response of annual volume increment to fertilization was not statistically significant until the fourth period. Yet, volume increases of the fertilized plots were 25%92% greater than those of the nonfertilized plots from 1976 to 1996. Similarly, basal area increment was greater in lightly thinned plots than in unthinned plots from the second period on, until heavy mortality during 19962002. Basal area increment was greater in the heavily thinned plots from the fourth period on. Results indicate that red fir can respond to fertilization and thinning quickly and that both treatments speed stand development. In addition, fertilization increases the stand's carrying capacity. Therefore, forest managers can use these silvicultural practices to improve stand growth, to reduce fire fuels, and to accelerate stand development.
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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 | 0.001 | 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".