Modeling Artificial Neural Network of Insect’s Proliferation During Cocoa Beans Storage
Bibliographic record
Abstract
The cocoa bean is a grain which is the raw material for the economy of Côte d'Ivoire.Thus, throughout the bean value chain, particular attention is paid to quality.In this chain, storage remains an imported step.Indeed, insects are one of the pests causing enormous damage and losses in the conservation of stored grains.These insects are also present during the storage of cocoa beans.The proliferation of insects is due to several physico-chemical and environmental factors such as water content (Te), sugar content (TSu) and temperature (T° ) which interact in the bean ecosystem.This proliferation remains difficult to control and estimate.In this work, we invented a method based on neural networks to determine the evolution of insect density.Indeed, an Insect Dynamics Model (MDI) has been established.To validate the effectiveness of the proposed method, we have chosen as performance criteria the coefficient of determination R² =0.9982.This shows a good correlation of the experimental values and those predicted.This result was obtained, with an optimal 4-5-1 architecture selected by Akaike's information criterion.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 |
| 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".