Effects of loblolly pine litter, forest floor, and root exclusion on mineral soil carbon in a Florida Spodosol
Bibliographic record
Abstract
Quantifying soil organic carbon (SOC) inputs in the surface soil is a critical component for assessing the potential for carbon sequestration of managed pine forests. This study used a sequential exclusion of aboveground litter inputs (L, litter exclusion) and aboveground plus belowground inputs (LR, litter and root exclusion) to segregate carbon sources contributing to the development and maintenance of SOC in the surface soil supporting juvenile loblolly pine (Pinus taeda L.) in its rapid growth phase. The study spanned the 7th to 10th year of stand growth. Soil physical size fractions (>2 mm, ≤2 mm, 2000–250 μm, 250–150 μm, 150–53 μm, and <53 μm) were used to investigate the change in native SOC over time in the untreated control plots (UC, untreated control) and the effects of exclusion treatments. An accretion rate of 4.6 Mg SOC·ha−1 of soil·year−1 was observed in the fine earth fraction (≤2 mm), reflecting the rapid phase of stand growth. The accretion was primarily observed in the upper 10 cm of the soil. Treatment effects were most apparent in soil bulk density, SOC of the fine earth, and 150–53 μm size fractions. In general, changes in SOC observed in the L treatment was an intermediate increase between the UC and LR treatments, where only the removal of roots provided no change in SOC and was significantly different from the control (p = 0.05). We conclude that a major contributor to the maintenance and increase of SOC in this fast-growing pine ecosystem was due to root turnover (60%), with 40% due to aboveground litter inputs.
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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) | 0.001 | 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".