Survey of rice (<i>Oryza sativa</i> L.) production ecosystems in northern Ghana confirms low risk of exposure to potential toxic elements from local grain consumption
Bibliographic record
Abstract
Expanding local rice production to meet consumer demand is a priority action under the Government of Ghana’s Planting for Food and Jobs initiative. While studies on yield-enhancing interventions abound, fewer studies focus on food safety issues (e.g., the potential toxic element status of the production ecosystems). This study was, therefore, conducted to bridge the knowledge gap. Chemical analyses were conducted on water, soil, and rice grain samples from different production ecosystems in the Northern and Upper East regions of Ghana. Statistical analysis of the data showed that soil and rice grain arsenic concentrations were significantly higher ( P < 0.001) in the Upper East region. In the Northern region, mean cadmium concentration in rice grains from the irrigated lowland fields (0.023±0.003 mg/kg) was significantly higher than in grains from the rainfed fields. All recorded concentrations of rice grain arsenic, cadmium, and lead were, however, within permissible limits, indicating a low risk of dietary exposure. The observed differences in concentrations within and between regions suggest that soil texture and other geogenic factors could influence the potential toxic element status of the rice production ecosystems. Regular monitoring is, therefore, recommended to maintain the safety of Ghana’s locally produced rice for human consumption.
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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 | 0.000 | 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".