Evaluation of the Level of Mercury Pollution in the Sediments of the Rivers Draining the Gold Panning Sites in the Territory of Fizi, Eastern Democratic Republic of Congo
Bibliographic record
Abstract
The sediments collected respectively from the Etó, Kacumvi, Kimbi, Lubichako, Makungu, Kuwa, Mandje, Misisi and Kimuti Rivers draining the gold panning sites in the Fizi territory were studied during a 16-month cycle (August and December 2016 to August and December 2017) in order to assess their degree of mercury pollution in the dry season as well as in the rainy season. The assessment of the degree of pollution of the said sediments focused on six parameters including the total mercury content (THg) and the indices of mercury pollution such as the mercury enrichment factor (EF), the mercury contamination factor (CF), the mercury geoaccumulation index (Igeo), the mercury potential ecological risk factor (PERF) and the mercury ecological risk index (ERI). Total mercury was determined by atomic absorption spectrophotometry (AAS) while the mercury pollution indices were successively calculated using the appropriate formulas. The results thus obtained revealed that all the sediments of the rivers studied are considerably polluted by mercury according to the values relative to their total mercury content and mercury pollution indices, including the mercury enrichment factor (EF), the mercury contamination factor (CF), the mercury geoaccumulation index (Igeo), the mercury potential ecological risk factor (PERF) and the mercury ecological risk index (ERI), which greatly exceed the standards recommended by the Canadian Council of Ministers of the Environment. In particular, the sediments of the Kimbi River are highly polluted by mercury compared to those of other rivers studied. This reported pollution is the result of anthropogenic gold panning activities that generate effluents and elemental mercury that pollute the streams.
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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 | 0.001 | 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) | 0.000 | 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".