Bibliographic record
Abstract
A litter sandwich approach was used to examine the loss of mass, acidity, and nutrients through 10 years of decomposition in a stand of loblolly pine (Pinus taeda L.). Each year, a new layer of 3-mm mesh fiberglass was placed on the annual accumulation of litterfall. Ten years of decomposition led to a loss of about 80% of the litter organic matter (giving a decomposition constant of 0.1655), which predicted a steady-state mass of the forest floor within 10% of the observed value. The pH (in 0.1 M KCl) showed little variation over time, ranging just from 3.2 to 3.5. The decline in titratable acidity appeared related primarily to the loss of organic matter rather than to any change in the acid characteristics of the material. Nitrogen loss was slow; the 10-year-old cohort of material contained 70% as much nitrogen as the original material. The loss of other nutrients was rapid, exceeding 80% loss by 6 years for phosphorus, potassium, calcium, and magnesium. The litter-sandwich method for examining decomposition is an easy, long-term approach that appeared to provide reasonable representation of the dynamics of unconfined forest floor materials. Future work should test this method for examining the decomposition effects of litter quality, nutrient supply, and environmental conditions.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".