Using pattern mining to determine fine climatic parameters for maize yield in Benin
Bibliographic record
Abstract
This study investigates the relationships between Benin's climate and maize production to develop an association rule algorithm for accurate yield prediction. The datasets utilized extend 26 years (1995 to 2020) and include climate and maize yield data from five districts with synoptic weather stations in two agroclimatic zones (Sudanian and Sudano-Guinean). Climate variables were combined with yield using "year" and "districts" to find the association rules. Several techniques were used to determine the correlation between weather parameters and maize yields: support vector machines, K nearest neighbor, artificial neural networks, decision trees, and recurrent neural networks. The most performed method was the decision tree (R2=0.998, mean squared error (MSE)=0.021, and mean absolute error (MAE)=0.0008). This model is difficult to understand, though the frequent pattern growth technique was then applied to the dataset to facilitate the discovery of the rules. The Sudano-Guinean zone exhibits high maize yields for medium minimum and maximum temperature values, rainfall, evapotranspiration, and humidity. In the Sudanian zone, medium minimum and maximum temperatures and maximum humidity levels are associated with high maize yields. The discovered association rules showed that optimizing maize output might be done dependably and effectively.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".