INFLUENCE OF THE WATER QUALITY OF LAKE TOHO LOCATED IN THE MONO DEPARTMENT, SOUTHWEST BENIN, BY GLYPHOSATE AND METALS (COPPER, ZINC, LEAD, CADMIUM)
Bibliographic record
Abstract
In order to assess the pollution status of the waters in Lake Toho, water samples were collected and analyzed using the spectrophotometer. Researched elements such as glyphosate are in high concentrations in the lake waters. The same goes for Trace Metal Elements such as copper, zinc, lead, cadmium in the waters of the said lake. The metal contents are beyond the tolerable limit by the Canadian standard following the Criteria for the protection of Aquatic Life according to Chronic effects (CVAC: 0.0085 mg.L-1 for copper and lead, 0.11 mg.L-1 for zinc, 0.0093 mg.L-1 for cadmium). Likewise, the copper, zinc and lead contents (during the long dry season and end of the long rainy season) recorded exceed the same standard according to the Criteria for the protection of Aquatic Life according to Acute effects (CVAA: 0.012 mg.L-1 for copper, 0.11 mg.L-1 for zinc and 0.22 mg.L-1 for lead). These levels of herbicide and metals present would contribute to the toxicity of the waters of Lake Toho and constitute a green threat for the aquatic organisms present in the said lake. It is urgent to put in place a management and control system for actions around the lake.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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".