Distribution of Zinc in Selected Benchmark Soils of South Western Nigeria
Bibliographic record
Abstract
The Understanding of the different fractions of Zinc in soils is important to effectively manage fertilizer resources due to the low availability of Zinc in the native soil worldwide. Bioavailability, uptake of Zinc by plant and its fractions depend largely on the soil parent material, the type of chemical transformation the soil has been expose to over times and some anthropogenic intervention. This study examined five different Zn pools.The distribution of Zn in soil fractions was determined for seven selected soils of South Western Nigeria. A sequential batch extraction which had been modified was used to identify Zn fractions and were separated into: the extractable zinc (Ex-Zn), zinc associated with the carbonate (CO3-Zn), the organically bound zinc (Org-Zn), sesquioxide (Ox-Zn) and residual (Res-Zn) in each soil. Total Zn was estimated as a sum of all the pools.Result showed that Zinc fractions in the soils were in this order: extractible pool (3.8%) < organic pool (13.6%) < carbonate pool (14.8%) < sesquioxide pool (22.8%) < residual pool (45.1%). The residual pool amounts for almost half of the zinc that made up the total zinc in the soil. The distribution of zinc into these pools were determined by selected physical and chemical properties of the soil; the pH, organic carbon, clay, CEC, and phosphorus. However, soil phosphorus and pH had the highest influence on the zinc in the experimental soils. The stable fraction that dominated the soil was evident in the low extractable Zn fractions in the soil. Which is an indication of the inherently low levels of the bioavailability of Zn in the selected soils used in this study.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| 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 teacher head, 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".