Nitrogen mineralization in short-rotation tree plantations along a soil nitrogen gradient
Bibliographic record
Abstract
We measured soil nitrogen (N) mineralization along an N fertilization gradient (control; irrigation only (I + 0 N); irrigation with 56 (I + 56 N), 112 (I + 112 N), and 224 (I + 224 N) kg N·ha1·year1, respectively) in 7-year-old cottonwood (Populus deltoides Marsh.), cherrybark oak (Quercus falcata Michx. var. pagodifolia Ell.), American sycamore (Platanus occidentalis L.), and loblolly pine (Pinus taeda L.) plantations established on a well-drained Redbay sandy loam (a fine loamy, siliceous, thermic Rhodic Paleudult), in Florida, USA. Nitrogen mineralization was measured monthly for 1 year, beginning in April 2001, with the buried bag incubation technique. Irrigation alone or fertigation (irrigation + N) affected annual net N mineralization rates under hardwood species, but no effect was found under loblolly pine. Overall, the rates were higher under cherrybark oak (108 kg N·ha1·year1) and cottonwood (101 kg N·ha1·year1) than under sycamore (82 kg N·ha1·year1) and loblolly pine (75 kg N·ha1·year1). Significant correlations were observed between N mineralization and stem volume in all species but loblolly pine. These results suggest that N mineralization response to irrigation or fertigation (irrigation + N) is heavily dependent on species-specific feedback mechanisms. Our results also support the hypothesis that the N mineralization versus productivity relationship is a fundamental feature of forests, resulting from the impact of N availability on productivity and the long-term feedback effects of vegetation on N availability.
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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) | 0.000 | 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".