Bibliographic record
Abstract
Pollutants from industry, mining, agriculture, and other sources have contaminated sediments in many surface water bodies. Sediment contamination poses a severe threat to human health and environment because many toxic contaminants that are barely detectable in the water column can accumulate in sediments at much higher levels. The purpose of this study was to make optimal treatment and disposal plan o( sediment for water quality improvement in small-scale resevoir based on an evaluation of degree of contamination. The degree of contamination were investigated for 23 samples of 9 site at different depth of sediment in small-scale J river. Results for analysis of contaminated sediments were observed that copper concentration of 4 samples were higher than the regulation of hazardous waste (3 mg/L) and that of all samples were exceeded soil pollution warning levels for agricultural areas. Lead and mercury concentration of all samples were detected below both regulations. Necessary of sediment dredge was evaluated for organic matter and nutrient through standard levels of Paldang lake and the lower Han river in Korea and Tokyo bay and Yokohama bay in Japan. The degree of contamination for organic matter and nutrient was not serious. Compared standard levels of Japan, America, and Canada for heavy metal, contaminated sediment was concluded as lowest effect level or limit of tolerance level because standard levels of America and Canada was established worst effect of benthic organisms. The optimal treatment method of sediment contained heavy metal was cement-based solidification/stabilization to prevent heavy metal leaching.
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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".