Long-term effects of weed control and fertilization on the carbon and nitrogen pools of a slash and loblolly pine forest in north-central Florida
Bibliographic record
Abstract
The effects of fertilization, weed control, and fertilization plus weed control on vegetation and soil C and N pools were examined for a loblolly pine ( Pinus taeda L.) and slash pine ( Pinus elliottii var. elliottii Engelm.) forest at ages 18 and 26 years (at the end of rotation). The total C accumulated in fertilized forests without weed control was 20% (slash pine) and 40% (loblolly pine) greater than in the control forests at the end of rotation. Weed control increased pine C pools at 18 years, but by the end of rotation, weed control effectively resulted in no gain in ecosystem C. When the two treatments were combined, weed control slightly subtracted from the net C benefit produced by fertilization. This result occurred because of decreased forest floor and soil C in the weed control plots. Fertilization significantly increased stem, foliage, forest floor, and soil N pools, and N retention was 63% and 103% of the applied N in the slash and loblolly pine forests, respectively. Weed control with fertilization reduced ecosystem N retention efficiency, but weed control alone did not negatively affect ecosystem N accumulation. These results suggest that the optimal treatment for increasing C accumulation and N retention in these ecosystems is fertilization without weed control.
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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".