Numerical modeling of glyphosate transfer to underground water: Application to the Djuttitsa watershed in the Bamboutos mountain, West-Cameroon
Bibliographic record
Abstract
The world population increases at an alarming rate, with about 52% of this population expected to be found in urban areas in 2025. Africa has the highest population growth rate in the world, estimated as 2,55% each year from 2010 to 2015. By 2050, Africa is expected to represent about four quarter of the world's population estimated as 1,3 billion inhabitants (AFD, 2014). Therefore, modern agricultural practices become an important challenge for food security (Abass et al., 2016). To increase food yield, Africa is faced by an excessive usage of pesticides without respect of recommended doses and frequency of application (white and Bunn, 2017). Soil and water pollution are direct consequences of this, with levels more than the authorised levels of 0.1 and 0.5 for maximal concentration of an individual pesticide and the total of all pesticides in water (Sousa et al., 2018). This shift from normal concentrations can induce severe consequences on population health (cancers, infertility, developmental abnormalities, neurotoxicity) (Huang et al., 2018). Pesticides intoxication is estimated at about 1 to 5 million cases per year in the world, with about 220 000 deaths each year. Developing countries use only 25% of the pesticides produced in the world but they account for 99% of deaths in the world due to pesticides poisoning especially in rural areas. To follow the evolution of pesticides in the soil and evaluate the risk of pollution, researchers have developed some numerical transfer models. However, the use of these models remains difficult in Sub-Saharan Africa due to insufficient data and inability to get some parameters. In this study, we therefore developed a 1D numerical model which was tested to study the glyphosate transfer at the watershed of Djuttitsa in West region of Cameroon. Their validation was done after statistical comparison of concentrations obtained from the model to those from the laboratory.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".