Comparison of Shredded Tire Chips and Tire Crumbs as Packing Media in Trickling Filters
Bibliographic record
Abstract
Abstract A five stage study was conducted using two trickling filters, one with shredded tire chips (12 to 50 mm) and the other with tire crumbs (1.5 to 6.5 mm) as packing media, and both landfill leachate and synthetically prepared leachate, to evaluate treatment performance. Due to increased surface area and sorption capacity, compared with other materials, a thick layer of biomass developed over the surface of tire chips and crumbs and sloughed off after approximately 21 days. Biochemical oxygen demand, chemical oxygen demand, and ammonia nitrogen removal were in the range of 81 to 96%, 76 to 90%, and 15 to 68%, respectively, under stable conditions. Organic removal and total dissolved solids reduction from the leachate were well correlated, with the exception of when biomass sloughing caused an increase in the organic content. The trickling filter with tire crumb media exhibited a more consistent organic removal throughout the experimental program. Tire chips, being readily available, could be a better alternative to crushed stone or gravel as a packing media in trickling filters. Tire crumbs appeared to be promising for small scale treatment systems.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".