Spatial heterogeneity of soil chemical properties in a lowland tropical moist forest, Panama
Bibliographic record
Abstract
We evaluated spatial heterogeneity for pH and a comprehensive set of nutrient and trace elements in surface (0–0.1 m depth) and subsurface (0.3–0.4 m depth) soils across 26.6 ha of old-growth, lowland, tropical moist forest, established on a highly weathered soil in Panama. Little is known about spatial heterogeneity patterns of soil properties in tropical forest soils. Soil was moderately acidic (pH 5.28) with low concentrations of exchangeable base cations (13.4 cmolc/kg), Bray-extractable PO4 (2.2 mg/kg), KCl-extractable NO3 (5.0 mg/kg), and KCl-extractable NH4 (15.5 mg/kg). The coefficient of variation for soil properties ranged from 24% to >200%, with a median value of 84%. Geostatistical analysis revealed spatial dependence at a scale of 10–100 m for most of the soil properties; however, pH, NH4, Al, and B had spatial dependence at a scale up to 350 m. Best-fit models to individual variograms included random, exponential, spherical, Gaussian, linear, and power functions, indicating many different spatial patterns among the set of soil properties. Correlation among individual elements was poor, indicating independent patterns. Our results show complex spatial patterns in soil chemical properties and provide a basis for future investigations on soil–plant relationships and soil nutrient niche differentiation.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".