Soil sampling size estimates for soils under teak (<i>Tectona grandis</i> Linn. F) plantations and natural forests in Ashanti Region, Ghana
Bibliographic record
Abstract
The variability of forest soil properties and the number of samples required to achieve desired levels of precision for estimation of property means have received little attention in the tropics. Highly variable forest soil properties require more intensive sampling and often have less predictive value for site assessment purposes. Sites at Offinso and Juaso Forest Districts in the Ashanti region, Ghana, were used to study the variability patterns for selected physical and chemical properties. Sites selected for this study were in the moist semi-deciduous forest zone and had nearly identical physiographic characteristics. A simple random sampling procedure was used to obtain soil samples at each site. In each of three natural forest stands and three teak plantations, 16 soil pits were examined and soil samples from the 0- to 20-cm (major rooting depth) and 20- to 40-cm depths were analyzed for selected chemical and physical properties. In the 0- to 20-cm depth, coefficients of variation varied from 8% (pH) to 72% (available P), and in the 20- to 40 cm depth from 16% (pH) to 116% (available P) under teak plantations. Similarly, in the 0- to 20-cm depth coefficients of variation varied from 11% (pH) to 40% (exchangeable K) and in the 20- to 40-cm depth from 10% (bulk density) to 86% (available P) under natural forests. Under both cover types, more samples were required to estimate means at ±10% allowable error with a confidence level of 95% for chemical properties than for physical properties. Key words: Tectona grandis plantations, moist semi-deciduous forest zone, Ghana, soil physico-chemical properties, forest ochrosol
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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 | 0.003 |
| 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 | 0.001 | 0.001 |
| 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".