The effects of vegetation control and fertilization on net nutrient release from decomposing loblolly pine needles
Bibliographic record
Abstract
This study examined the effects of vegetation control and nitrogen + phosphorus fertilization on decomposition and nutrient release dynamics of loblolly pine (Pinus taeda L.) needle litter. Needle litter was placed in litterbags and left to decompose on the forest floor, and changes in mass loss and nutrient (N, P, K, Ca, Mg, S, Mn, Zn, B, Cu) concentrations and contents were observed at 2- to 6-month intervals for 32 months. Fertilization had no effect on mass loss, while vegetation control resulted in a warmer and drier forest floor and led to reduced mass loss (k = 0.39 and 0.28 year1for fertilization and vegetation control, respectively). Concentrations of N, P, Ca, S, Zn, and Cu in the decomposing litter increased two- to three-fold over the 32 months, while concentrations of K, Mg, Mn, and B declined, increased, or did not change depending on time and treatment. Based on the release dynamics, the nutrient mobility series was as follows: Cu [Formula: see text] N [Formula: see text] S < P < Zn [Formula: see text] Ca < K [Formula: see text] Mn < Mg [Formula: see text] B. Fertilization had no effect on release dynamics; however, vegetation control reduced release of N, P, S, and Zn, and increased release of B. The mineral soil may be the main source of plant available N and P in midrotation southern pine stands based on the slow release of these elements from decomposing needle litter.
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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".