Freeze–thaw treatment of RBC sludge from a remote mining exploration facility in subarctic Canada
Bibliographic record
Abstract
Freeze-thaw conditioning of RBC (Rotating Biological Contactor) sludge was tested using a pilot-scale freezing bed placed in a mobile freezer operated at -10°C. Sludge samples from a remote mining exploration facility were flown in every 2 weeks, and added to the freezing bed in 8 layers of 10 cm thick. Approximately 4 months after the first layer of sludge was added, the pilot unit was removed from the freezer and thawed at ambient temperatures. After one day of thawing, the solids concentration increased from 2.6% to 16.2%. The final cake solids concentration was 21%. Melt water had increasing turbidity, COD, TSS, VSS, nitrogen and phosphorus concentrations during the thawing period. Freeze-thaw conditioning also decreased the initial densities of fecal coliforms and Salmonella in sludge. The results of this study showed that freeze-thaw technology successfully dewatered RBC sludge without the need for mechanical equipment, and is a sustainable option for sludge dewatering in cold and remote regions.
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 | 0.001 | 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) | 0.000 | 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 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".