Removal and Re‐use of Tar‐contaminated Sediment by Freeze‐dredging at a Coking Plant Luleå, Sweden
Bibliographic record
Abstract
Submerged tar-contaminated sediment are generally very loose, which makes remediation challenging. We tested if a modified version of freeze-dredging could be used to remove and dewater such sediment in a canal down-stream a coking plant. PVC hoses carrying a heat medium were placed horizontally in the submerged sediment. Five days of freezing allowed straightforward removal of most of the sediment. Flat freeze cells were placed side by side in the canal to remove the rest. The freeze-thaw process increased the dry substance content from approximately 50 to 80%. Outdoors storage under rainy conditions did not re-wet the dried sediment. The material was successfully used as feed-stock in the coking plant, with the double cost-benefit of avoided transportation to deposit and reduced use of coal. The study demonstrates that freeze-dredging can facilitate removal, storage and beneficial re-use of submerged tar-contaminated sediment.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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".