SEASONAL VARIATION, DISTRIBUTION AND CHARACTERISTICS OF MICROPLASTIC IN SEWAGE SLUDGE
Bibliographic record
Abstract
Microplastic particles (MPs) pollution is widespread in the oceans, atmosphere, and soils due to the diverse applications and origins of plastic. Microplastic particles were found in marine animals and birds organisms, moreover recent research even have shown that MPs are detected in human blood [1]. Nowadays, microplastics are considering as an emerging global pollutant. Recent research has noted that plastic pollution is greatly influenced by seasonal variation and especially the amount of precipitation. However, there is a knowledge gap regarding microplastic pollution pathways by seasonal variation. Microplastic particles enter the environment easily and can accumulate in a variety of biological systems. The distribution of microplastic particles in wastewater treatment plants (WWTP) and their entry into the environment is a critical area of microplastic particle research. 60 % – 99 % of MPs from different sewage sources are detained in sludge [2]. Due to its valuable organic composition, sludge is widely used for agricultural purposes, especially for soil fertilization. For instance, Norway applies about 80 % of sewage sludge in agriculture, Ireland – 60 %, JAV and Canada 45 – 55 % [3]. When MPs are mixed with the soil matrix, the additives contained in the microplastic particles can be released and participate in the chemical and biological processes in the terrestrial environment, especially in the cycling of soil elements. It has been reported that sewage sludge containing a high concentration of MPs may affect water and nutrient uptake processes in crops and cause a negative effect on their growth [4]. This work presents the identification and characterization of MPs in sewage sludge collected from the WWTP in each season. Results review the abundance of microplastic particles in size range from 20 µm to 1000 µm and analyze concentration, classification, morphological properties, and chemical composition of MPs extracted from sewage sludge. Also see: https://micro2022.sciencesconf.org/425766/document
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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".