Sustainable Use of Chemical in Agricultural Soils and Implications for Precision Agriculture
Bibliographic record
Abstract
This study characterized and assessed the geostatistical variations of some essential macronutrients (Ca, P, Fe, Na, K, Al, Mg and Ti) for further environmental monitoring, planning and remediation using geochemical analysis in two commercial farms. Twenty soil samples were collected at the depth of 50 cm to 70 cm below the subsurface from the study areas that is, Landmark university farm representing the northcentral and Covenant university farm representing southwest Nigeria, respectively. Inductively coupled plasma and mass spectrometry (ICPMS) was used to analyze the samples at the Acme laboratory, Canada. The statistical results indicate that the following pair of elements {Ca-Mg, P-Mg, Fe-P, Fe-Al, Ca-K, Mg-K, and Na-K} are significantly positively correlated at 0.05 significance in the areas. The mean and median test revealed that iron (Fe) and titanium (Ti) content are the same in both study areas. The findings among others imply that deficient essential nutrients can be applied as fertilizers to farmland and thereby enhancing sustainable agricultural production.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".