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Record W2056050521 · doi:10.1080/09593330801987566

USE OF SHREDDED TIRE CHIPS AND TIRE CRUMBS AS PACKING MEDIA IN TRICKLING FILTER SYSTEMS FOR LANDFILL LEACHATE TREATMENT

2008· article· en· W2056050521 on OpenAlexaff
Bibek Mondal, M.A. Warith

Bibliographic record

VenueEnvironmental Technology · 2008
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Soil Stabilization
Canadian institutionsToronto Metropolitan UniversityGolder Associates (Canada)
Fundersnot available
KeywordsTrickling filterLeachateWaste managementEnvironmental scienceChemical oxygen demandCloggingEffluentPulp and paper industryBiomass (ecology)Environmental engineeringWastewaterEngineeringEcology

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.314
Threshold uncertainty score0.555

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.024
GPT teacher head0.187
Teacher spread0.162 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations25
Published2008
Admission routes1
Has abstractyes

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