Soil and Douglas-fir (<i>Pseudotsuga menziesii</i>) foliar nitrogen responses to variable logging-debris retention and competing vegetation control in the Pacific Northwest
Bibliographic record
Abstract
Experimental treatments of logging-debris retention (0%, 40%, or 80% surface coverage) and competing vegetation control (initial or annual applications) were installed at two sites in the Pacific Northwest following clearcutting Douglas-fir ( Pseudotsuga menziesii (Mirb.) Franco var. menziesii) stands to assess short-term effects on tree N acquisition, soil N supply, and total soil N. Vegetation control treatments began in the first year after harvest, and logging-debris manipulations were installed 2 years after harvest. Annual vegetation control increased foliar N concentration and content in most years at both sites, which was associated with higher available soil N and increased soil water content. Logging-debris retention treatments had no detectable effect on any of the foliar variables or soil available N at either site. There were no treatment effects on total soil N at the site with relatively high soil N, but total soil N increased with logging-debris retention when annual vegetation control was applied at the site with a low initial soil N pool. Competing vegetation control is an effective means to increase tree N acquisition in the initial years after planting while maintaining soil N pools critical to soil quality. The effect of logging-debris retention on tree N acquisition appears to be limited during early years of stand development, but increased soil N with heavy debris retention at certain sites may be beneficial to tree growth in later years.
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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".