Evidence of uptake of different pollutants in plants harvested from soil treated and fertilized with organic materials as source of soil nutrients from developing countries
Bibliographic record
Abstract
Abstract The use of organic materials as soil nutrients to improve agricultural production is well documented. However, these organic materials may contain toxic pollutants that may bio-accumulate in plant tissues and eventually be consumed by humans. There is a misconception about the use of organic materials (sludge, urine, human waste and urban waste) in agriculture and organic farming. The review work examined the sources and uses of organic material in agriculture from developing countries and the dangers posed by the use of polluted organic materials in agriculture. The review examined through literature the availability and uptake of pollutants in crops that are cultivated from farming activities using organic materials. The review established the possibility of uptake of pollutants from treated waste materials that are used for farming. Some of the pollutants that can be bio-accumulated by plants when cultivated on soil containing these pollutants were documented. The review concluded by establishing the need to create awareness on the possible health risks associated with the use of organic materials if the materials used were polluted. The review also highlighted the importance of educating peasant farmers on the dangers associated with collecting waste materials from untreated sources.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".