The use of various soil and site variables for estimating growth response of Douglas-fir to multiple applications of urea and determining potential long-term effects on soil properties
Bibliographic record
Abstract
Estimating the growth response of Douglas-fir ( Pseudotsuga menziesii (Mirb.) Franco) stands after nitrogen (N) fertilization is difficult because of the high site variability present in the Pacific Northwest. Our objective was to determine how site and soil variables relate to stand response to repeat applications of 224 kg N·ha–1 as urea once every 4 years. The unstandardized residuals of two dependent variables (total cumulative volume and 4-year periodic annual increment, or PAI) were regressed against site and soil variables using stepwise regression. Data were stratified by three different stand density treatments: unaltered stand density (SD), one-half SD (SD/2), and one-quarter SD (SD/4). Both total cumulative volume and 4-year PAI after the second application of urea was significantly higher in the fertilized plots (p = 0.008; 0.009), whereas only total cumulative volume was significant after the third fertilizer application (p = 0.021). Thinning effects were highly significant (p < 0.001) for all three fertilizer applications. The strongest related stand, site, or soil variable to fertilization response existed between percent N at the 30–50 cm depth and total cumulative volume (R2 = 0.833) for the SD/2 stand density management regime. Regression analysis showed that C, N, NH4+, and NO3– concentration data explained the most variation, while stand and site variables contributing the least. The results demonstrate that multiple applications of urea provide significant increases in total volume, but effects of successive applications diminish over time.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".