Characterization of Contaminated Sediments in Hamilton Harbour, Lake Ontario
Bibliographic record
Abstract
Sediments in many harbors and connecting channels of the Great Lakes have been negatively impacted by industrial, agricultural and municipal discharges and by atmospheric deposition. Contaminated sediments may pose risks to both human health and to aquatic environments. All 42 U.S. and Canadian Areas of Concern (AOCs), designated by the International Joint Commission (IJC), have problems associated with the occurrence of contaminated sediments. Remedial Action Plans, which have been prepared for these areas, include sediment remediation plans. Appropriate and cost-effective sedimentological, geotechnical and geochemical investigations are required to determine the degree and extent of contamination. The methodology used in sediment characterization includes in-situ penetration tests, core sampling, acoustic bottom-classification systems, side-scan surveys and in-situ porewater samplers. Contaminated sediments are typically characterized for chemical concentrations of contaminants of interest in terms of both areal extent and vertical distribution. Site specific examples of sediment analyses and characterization for remediation projects are discussed for three sites within Hamilton Harbour, Ontario, where very heterogeneous sediments exist.
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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 | 0.000 |
| 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) | 0.002 | 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".