USE OF SHREDDED TIRE CHIPS AND TIRE CRUMBS AS PACKING MEDIA IN TRICKLING FILTER SYSTEMS FOR LANDFILL LEACHATE TREATMENT
Bibliographic record
Abstract
Scrap tire stockpiles are breeding grounds for pests, mosquitoes and west Nile viruses and, thereby, become a potential health risk. This experimental study was carried out in six stages to determine the suitability of shredded tire materials in a trickling filter system to treat landfill leachate. Biochemical oxygen demand (BOD5), chemical oxygen demand (COD) and NH3-N removals were obtained in the range of 81 to 96%, 76 to 90% and 15 to 68%, respectively. The removal of organics appears to be largely related to total dissolved solids reduction in leachate. A sudden increase, from time to time, in organic content of effluent could be attributed to biomass sloughing and clogging in the trickling filters. However, tire crumbs exhibited more consistent organics removal throughout the experimental program. Due to the high surface area of shredded tire chips and crumbs, a layer of biomass, 1-2 mm thick, was attached to them and was sloughed off at an interval of 21 days. Apart from that, as shredded tires are comparatively cheaper than any other usable packing material, tire chips and tire crumbs appeared to be quite promising as packing media in trickling filters for landfill leachate treatment.
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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 | 0.000 | 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) | 0.000 | 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".