Analysis and Effects of Microplastics in the Agricultural Soils
Bibliographic record
Abstract
Microplastics (MPs) are pertinacious contaminants which are threatening the delicate balance of the ecosystem.Over the years, various studies have reported the presence of MPs in the marine environment through case studies and reviews, but its presence in the agricultural soils is not fully understood.To date, there exists no standardized methodology for sampling, extraction and characterization of MPs in the agricultural soils.This review aims at encapsulating the different techniques used for sampling and extraction of MPs, and includes the methods employed for identification and quantification of MPs in the soil environment.Flotation method, pressurized fluid extraction and elutriation are some of the different techniques used for MPs extraction that usually influence the characterization step.Visual identification through microscope and scanning electron microscope and chemical identification via Fourier-transform infrared (FITR) and Raman spectrometry, are the main techniques that are used for the identification of MPs.The fate and dispersion of MPs in the agricultural soil are discussed, as well as their interaction with the soil biota.The main findings of this review show that further research is needed on the sampling methods as well as analytical techniques so as to contribute to standardizing MPs characterization in the soil environment, potential toxicity effects along the food chain, and global policies for sustainable development.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| 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) | 0.001 | 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".