Dissolved carbon and nitrogen leaching following variable logging-debris retention and competing-vegetation control in Douglas-fir plantations of western Oregon and Washington
Bibliographic record
Abstract
We examined the effect of logging-debris retention and competing-vegetation control (CVC, initial or annual applications) on dissolved organic carbon (DOC), dissolved organic nitrogen, and nitrate-N leaching to determine the relative potential of these practices to contribute to soil C and N loss at two contrasting sites. Annual CVC resulted in higher soil water nitrate-N concentration and flux, with the magnitude and duration of the effect greatest at the high-N site. Most of the increase in nitrate-N at the low-N site occurred in treatments where logging debris was retained. Dissolved organic nitrogen increased at the high-N site in March of each year following annual CVC, but the contribution of this increase to total N concentration was small (2%–4% of total N flux). There was no effect of logging-debris retention or CVC treatment on soil water DOC concentrations, indicating that DOC inputs from logging debris and competing vegetation were either retained or consumed in the mineral soil. The estimated increase in leaching flux of dissolved C and N associated with the treatments was low relative to total soil pools, making it unlikely that loss of these elements via leaching will negatively affect future soil productivity at these sites.
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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 | 0.001 | 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".