Treatability study of two hybrid-passive treatment systems for landfill leachate operated at cold temperature
Bibliographic record
Abstract
Cold ambient temperatures can negatively affect the performance of passive and semi-passive landfill leachate treatment systems and decrease treatment efficiency. Cold temperature leachate treatment efficiencies were compared between a commercially available semi-passive treatment system and a passive peat and wood shaving biological trickling filter. The addition of an active fixed-film pretreatment stage in the treatment train was also assessed. Results indicated that the internal temperature of the peat filters was independent of influent water temperature; exothermic reactions maintained internal system temperatures. It was determined that pretreatment of the leachate did not affect the overall removal of chemical oxygen demand (COD), but did increase nitrification in the subsequent passive treatment systems and allowed for the removal of dissolved inorganic constituents prior to the passive treatment system, which will extend the useful life of the entire treatment train. The hybrid-passive treatment systems reduced COD concentrations by 10 ± 3% and 15 ± 3%, in the semi-passive treatment system and the peat and wood shaving biological trickling filter-based systems, respectively, and indicated that nitrifying biomass was starting to populate the treatment systems. It was therefore concluded that operation of these systems would be feasible under cold climate and should be assessed at the pilot-scale.
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 | 0.000 | 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) | 0.001 | 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".