Spatial variation in soil organic carbon and stable carbon isotope signature in a pasture and a primary forest in central Panamá
Bibliographic record
Abstract
Soil properties and their spatial variability was measured to provide a strong database to assess the modification in soil properties associated with future changes in land use. Surface (0--10 cm) soil samples were collected from a 9 ha, 46-year-old pasture being converted to a native tree plantation and a neighboring control pasture near Sardinilla, Panama. A small-scale nested grid of surface soil samples was replicated in the future plantation and a primary forest in the region to evaluate the spatial variability of soil properties. Seven 1 m profiles were sampled in the future plantation and litter samples were collected at the forest and the future plantation. Assuming the three sites were identical before the conversion to pasture, the difference in surface soil organic carbon (SOC) was 0.75 kg m-2 or equivalent to a loss of 0.017 kg m-2 yr -1 since the original land-use change. The control pasture is higher in bulk density but lower in percent SOC than the future plantation, which is critical to future comparisons. The pasture soils showed signs of soil compaction and of the homogenization of soil properties. For the 7 profiles in the future plantation, total SOC mass ranged from 13.45 to 23.80 kg m-2, and stable isotopes revealed that 82% of the SOC in the top 10 cm is derived from the pasture vegetation, down to 23% at 1 m depth. Spatially, the full scale of spatial variability was not determined from the nested grids alone. In addition, the 15 x 15 m grid in the future plantation generally over-estimated the semivariance at the scales below 3 m. The nested grids assessed the minimum measurable semivariance below 5 m. The precision of surface estimations from point observations can be improved by adding a 5 x 5 m grid to any large-scale sampling scheme.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".