Water Pollution and Agriculture Pesticide
Bibliographic record
Abstract
The agricultural industry uses substantial amounts of water (the highest in the world) mostly for irrigation purposes. Rapid population growth and, consequently, growing demand for food have increased the use of pesticide to have higher yield for crops and other agricultural products. Wastewater generated as a result of excessive use of pesticides/herbicides in agricultural industry is becoming a global issue specifically in developing countries. Over 4,000,000 tons of pesticides are currently used in the world annually and high concentrations above their threshold limits have been detected in water bodies worldwide. The generated wastewater (contaminated with pesticides) has negative impacts on human health, the ecosystem, and the aquatic environment. Recently, biodegradable and biocompatible (including plant-based) pesticides have been introduced as green and safe products to reduce/eliminate the negative impacts of synthetic pesticides. Despite positive advantages of biopesticides, their use is limited due to cost and slow interaction with pests compared to chemical pesticides. Pesticides may also react with water and constituents of soil resulting in formation of intermediates having different physical and chemical properties. Diffusion, dispersion, and permeation are main mechanisms for transfer of pesticides in soil and water. Pesticides may degrade naturally in nature; however, the time requirement can be very long. Many mathematical models have been developed to simulate and estimate the final fate of pesticides in water resources. Development of new technologies and environmentally friendly pesticides to reduce water contamination is becoming increasingly important.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".