Application of natural and enhanced natural attenuation of heavy metals in soils and sediments
Bibliographic record
Abstract
Various techniques can be considered for the remediation of contaminated sediments. The options can include capping, dredging, or physical, biological, and/or chemical treatments and natural recovery. Natural recovery could be beneficial over dredging due to a reduction in costs and lack of solid disposal requirements. Source control, however, is a major issue for sustainable remediation. In a case study, surface and core sediment samples were collected from a harbor on the north bank of the St. Lawrence River in the province of Quebec to assess heavy metal pollution and determine if natural recovery was occurring. Comparing the results of all analysis done for sediment for three different years (2015, 2017 and 2019) in the sampling area, it can be seen that some metals increased, some decreased and some of them showed nearly the same level of contamination. The results also indicated that during the sampling periods, copper, zinc and chromium were the main elements that exceeded the occasional effect level based on the Environment Canada sediment quality guidelines. Therefore, metal pollution has become a noticeable problem in this area and natural recovery was not achieved for several metals due to ongoing contamination and thus source control is critical.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".