Postharvest forest floor manipulation effects on nutrient dynamics in a loblolly pine (<i>Pinus taeda</i>) plantation
Bibliographic record
Abstract
The synchronization of nutrient release and demand in early stand establishment is important to maximizing resource use in forest plantations. We explored the impacts of forest floor manipulations on the dynamics of forest floor and mineral soil nutrient pools in a Pinus taeda L. plantation in North Carolina prior to and during 2 years following harvest and replanting. We present a novel method to estimate forest floor decomposition that avoids the exclusion of large detritivores. Decomposition and nutrient release rates from the forest floor were higher than rates typically observed in older stands (averaging 81% mass loss and 75% N loss across treatments over the 2-year period), highlighting the potential importance of the forest floor nutrient pool in early stand nutrition. Doubling the forest floor increased available C, N, and P pools in the mineral soil 46%, 47%, and 49%, respectively. Incorporating the forest floor into mineral soil through mixing had only transient positive effects on nutrient pools. Across treatments, an expected postharvest flush of soil available N was observed; however, removing the forest floor caused an earlier flush of available N in comparison with the control treatment, and doubling the forest floor caused a year delay in maximum N availability, better synchronizing the site’s available N with stand demand.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".