Effect of municipal sludge on the accumulation of organic pollutants in Ipomoea aquatic plant-paddy soil
Bibliographic record
Abstract
Ipomoea aquatic plant was grown in a pot of paddy soils with municipal sludge and chemical fertilizer. Seven kinds of organic pollutants including 43 compounds such as phthalic acid esters (PAEs), polycyclic aromatic hydrocarbon (PAHs), etc. in the Ipomoea aquatic plant and the soils were systematically analyzed with GC/MS to investigate the effect of the sludge and the fertilizers on accumulation of organic pollutants in Ipomoea aquatic plant and the soil. There were 28 compounds including PAEs, PAHs, chlorobenzenes, nitrobenzenes, ethers and halogenated hydrocarbons, etc. were detected in Ipomoea aquatic plant (amines not detected); and in pot soil 33 compounds were detected, in which PAEs and PAHs were predominant with their total content more than several or several ten times of the other pollutants. Contents of individual pollutant in different treatments of Ipomoea aquatic plant and pot soil varied greatly, with one or very few pollutants predominant. The contents of pollutants PAEs, PAHs, etc. in the plant and the soil fertilized with municipal sludge increased; strongly carcinogenic Benzo (a) pyrene was detected in Ipomoea aquatic plant and higher than Canadian soil standard (1.0mg/kg) in part of the soil. Except the bioconcentration factors (BCFs) of Foshan sludge treated Ipomoea aquatic plant to chlorobenzenes and ethers were more than 1.0, all the others were less than 1.0.
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 | 0.000 |
| 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".