Alterations in litter decomposition patterns in tropical montane forests of Colombia: a comparison of oak forests and coniferous plantations
Bibliographic record
Abstract
Understanding the alterations in litter decay patterns that follow changes in land use in tropical montane forests is essential for comprehending carbon, energy, and nutrient dynamics in this understudied ecosystem. The main objective of this study was to determine the changes in organic matter, carbon return, and nutrient cycling when oak forests are replaced by coniferous plantations in tropical montane forests. Five litter decay models (single, double, and triple pool exponential, gamma pk, log-uniform pk) were used to fit litter mass loss data over time. Although all models properly fitted the data, the triple pool exponential model was chosen because all parameters (coefficient of determination (R2), mean square of error (MSE), and Akaike information criterion (AIC)) were statistically the most adequate. Results indicated that litter of coniferous species decomposes more slowly than oak litter material, thus slowing the nutrient cycling. In this study, lignin content, C:N ratio, and N:P ratio were poor predictors of litter decomposition.
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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 | 0.001 | 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) | 0.001 | 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".