Investigating global warming's influence on food security in Benin: in-depth analysis of potential implications of climate variability on maize production
Bibliographic record
Abstract
Climate change has emerged as a pressing concern affecting nations worldwide, particularly within the African continent, including Benin. Given that maize stands as a staple cereal in Benin, this research to assess the impact and predict the effects of climate change on maize production by the year 2050. To attain this objective, an assortment of data encompassing climatic conditions, demographic factors, fertilizer application levels, and emissions of environmental pollutants has been collected and analyzed. The data analysis based on ARDL and ARIMA models has unveiled those variables such as emissions of CO2 and CH4 through food waste, peak temperatures, precipitation patterns, and rural population density exert considerable immediate influence over maize production volumes. The predictive models portend an upswing in national maize production volume, albeit accompanied by a probable decline in per capita availability. Policies aimed at controlling activities that generate high levels of air pollutants should be formulated to increase production capacities.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".