Optimizing the production of a Zea mays - Grevillea robusta alley cropping system with the WaNuLCAS model using a tabu search heuristic
Bibliographic record
Abstract
The Water, Nutrient and Light Capture in Agroforestry Systems (WaNuLCAS) and an intelligent optimization technique, namely a Tabu Search metaheuristic were employed to select the optimum management practices for a Grevillea robusta and Zea mays (maize) alley cropping system. This agroforestry system was simulated for Embu Research Station in the Central Highlands of Kenya where G. robusta is commonly grown. The objectives for this land-use system were to maximum tree and crop production and Net Present Value (NPV). The maize production was constrained not to fall below 75% of the first crop of a sole cropped maize cropping system. The decision variables that were determined included; the tree planting density, pruning management and fertilizer application in the even-aged alley cropping system. The WaNuLCAS model simulated crop growth successful but was found inadequate for tree growth. The Tabu search heuristic was found ideal for optimizing this alley cropping system.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".