Linear Programming-Based Optimization of Synthetic Fertilizers Formulation
Why this work is in the frame
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Bibliographic record
Abstract
Linear least-cost programming was used in formulating three NPK labeled synthetic fertilizers. Linear Programming technique was selected to formulate the appropriate composition of the three synthetic fertilizers mixes with least costs of production and optimum potential to increase yield of crops and soil fertility. Data about fertilizers specifications and constraints imposed on the fertilizers components were collected from 30 synthetic fertilizers plants. Costs of ingredients used in fertilizers formulation were obtained from the prevailing market prices. The results of the study prevailed that the least cost synthetic fertilizer combinations of the three studied fertilizer mixes among many calculated combinations was 672 JDs for the first mix with NPK label of 20-20-20, 669 JDs for the second mix with NPK label of 15-30-15, and 754 JDs for the third mix with NPK label of 12-12-36. These all three costs are lower by nearly 5-10% than those imposed by the market. These results confirm the importance of using techniques such as LP to formulate least cost products in industries such as synthetic fertilizers industry.
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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 | 0.000 | 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 | 0.000 | 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 it