Differences in the soil microbial community and carbon‐use efficiency following development of <i>Vochysia guatemalensis</i> tree plantations in unproductive pastures in Costa Rica
Bibliographic record
Abstract
This study shows that Vochysia guatemalensis tree plantations were associated with enhanced soil biotic and abiotic characteristics in previously cleared forests in the northern zone of Costa Rica, suggesting the possible use of this practice as a restoration strategy for local land owners. Soil samples from a primary forest, secondary forest, and a 13‐year‐old plantation of V. guatemalensis had greater relative abundances of DNA sequences of microbial genera critical for carbon‐use (C‐use) efficiency (i.e. the saprobe, complex C and wood rot/lignin decomposer fungi, and bacterial lignin and other complex C degraders), and greater levels of total organic carbon, C‐biomass, and microbial quotients as indicators of enhanced C‐use efficiency, than found in soils of adjacent 5‐year‐old V. guatemalensis plantations and abandoned non‐productive pasture/grasslands (GRs). The major research conclusions were that (1) conversion of forested land into abandoned pasture/GRs decreased the C‐use efficiency in the soils and the microbial groups associated with C‐use efficiency; (2) soils in plantations of V. guatemalensis were associated with increased abundances of the DNA of these same microbial groups and enhanced C‐use efficiency; (3) DNA‐based taxonomic analysis of microbes and analysis of the microbial quotient values can be used to monitor soil ecosystems for assessment of the efficacy of restoration activities. Thus, planting V. guatemalensis on damaged lands in the Maquenque National Wildlife Refuge should be encouraged to provide a sustainable forestry crop that can be harvested rotationally, while improving soil ecosystem health and reducing the pressure to harvest other forest sites.
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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".