Geochemical and physical characteristics of contaminated sediment in a harbour area
Bibliographic record
Abstract
Heavy metals adsorbed by sediments are of particular concern due to their mobility and toxicity in the aquatic ecosystem. Harbour areas, particularly on the banks of large rivers, have been facing deposition of polluted sediment. Considering a technique for remediation of contaminated sediment needs a comprehensive understanding of the geochemical and physical characteristics of sediment. In this study, a set of surface sediment samples was taken from a harbour on the bank of the St. Lawrence River, Quebec, Canada. The harbour area was polluted by heavy metals and there was an urgent need to dredge the sediments. However, prior to managing the sediments, the toxicity and availability of metals in sediment should be evaluated. Determination of the particle size distribution was performed in addition to pH, loss on ignition (LOI) and oxidation-reduction potential (ORP). To examine the mobility and dynamics of heavy metals in sediments, a sequential extraction technique was used. Cr, Ni, Cu, Zn, As, Cd and Pb were the elements investigated in this study. Results showed that the sediment samples were highly organic and the textures were pretty fine. The results also indicated that the copper, zinc and chromium were the main elements that exceeded the occasional effect level based on the Environment Canada sediment quality guidelines. However, the risk of mobility due to the availability of cadmium and lead was significantly more than the other elements. For example, the concentration of cadmium in a location was around 60% in exchangeable and carbonate fractions of the total. The most contaminated location was near the dock area, where usually receive the runoffs from the boat maintenance area. To conclude, the concentration and potential mobility of heavy metals in sediments near the dock area must be considered when determining the most appropriate management strategy.
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 | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".