Nitrogen mineralization dynamics following the establishment of a loblolly pine plantation
Bibliographic record
Abstract
The USDA Forest Service initiated a national study in the early 1990s to examine the effects of organic matter removal, compaction, and vegetation control on tree growth and soil processes at several locations across the United States and Canada. Our study was undertaken on the Lower Coastal Plain of North Carolina installation during the second and the fifth growing seasons following loblolly pine (Pinus taeda L.) plantation establishment. We used the in situ soil core incubation method to assess net N mineralization, and collections were conducted monthly from March to December in 1993 and 1996. The largest differences in N mineralization resulted from soil type differences between blocks. Organic matter removal did not affect N mineralization in either year; however, compaction reduced N mineralization during both years. Vegetation control had a pronounced positive effect on N mineralization and was only slightly less important as compared with soil type. Mineralization rates in year 5 were 80% less than in year 2. We hypothesized that the high N mineralization rates in year 2 may be related to a decrease in the input of soluble organic C following harvest (reducing immobilization), a quick mineralization of microbial N, fluctuating soil temperature and water conditions, and fine roots and litter biomass input following harvest.
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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 | 0.001 | 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".