Are forest floors in mid-rotation stands of loblolly pine (<i>Pinus taeda</i>) a sink for nitrogen and phosphorus?
Bibliographic record
Abstract
We examined decomposition and nutrient dynamics in fresh litter and net N mineralization in old litter to determine (i) if forest floor is a source of available nutrients in mid-rotation loblolly pine (Pinus taeda L.) stands and (ii) the effect of site preparation and vegetation control on forest floor nutrient dynamics. Four types of fresh litter were decomposed in situ in mesh bags: two containing only pine (differing by history of stand management) and two pinehardwood mixes (ratios 1:5 and 5:1). Litter bags were recovered every May and October for 26 months. Litter type, in particular the presence and amount of leaves, affected decomposition and nutrient dynamics in fresh litter. After 26 months, all fresh litters lost 55% of mass; decay rate constants were 0.43 (needles) to 0.60 year1 (leaves). Pine (1) and pine (2) and needles in mix 1:5 immobilized N. Almost all fresh litters also immobilized P. After 26 months of decay, N and P pools suggested an accumulation in the needles of 0.72.7 kg N·ha1 and 0.20.5 kg P·ha1 and mineralization of 1.13.7 kg N·ha1 in mix 5:1 needles and mix 1:5 leaves. All fresh litters mineralized K, Ca, and Mg. Old litter was incubated in situ in capped polyvinyl chloride containers from May to November. Monthly rates of net N mineralization in old litter were determined in KCl extracts of NH4+ and NO3. Net N mineralization in old litter was 0.751.5 kg N·ha1 per 6 months and was attributed to mineralization in mineral soil. We conclude that forest floors in these stands are not a source of available N or P. Instead, forest floors appear to be sinks for N and probably P.
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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".