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Record W1973469152 · doi:10.1080/09593332708618680

Biosolids from Two-stage Bioleaching could produce Compost for Unrestricted Use

2006· article· en· W1973469152 on OpenAlexaffabout
J.G. Henry, D. Prasad

Bibliographic record

VenueEnvironmental Technology · 2006
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicComposting and Vermicomposting Techniques
Canadian institutionsUniversity of Toronto
FundersMinistry of Education, IndiaMinistry of Earth Sciences
KeywordsBiosolidsCompostBioleachingChemistrySewage sludgeEnvironmental chemistryPulp and paper industryGreen wasteEnvironmental scienceFertilizerSewage treatmentWaste managementEnvironmental engineering

Abstract

fetched live from OpenAlex

A biological process, called BIOSOL, developed in our laboratory, efficiently reduces metals and reduces pathogenic indicator bacteria in sewage sludges in a single-stage system to levels meeting Ontario Ministry of the Environment (OME) requirements for land application of biosolids. Since the requirements for unrestricted use of compost are 10 to 50 times (depending on the metal involved) more restrictive than those for land application, this study was carried out to determine whether the efficiency of the BIOSOL system could be increased to meet the more stringent limitations required for compost. A two-stage modified BIOSOL system was successfully operated for a period of 4% months, at 30 degrees C, treating anaerobically digested sludge from the Guelph Wastewater Treatment Plant. Elemental sulphur (4g l(-1)) was the energy source for the autotrophic bacteria (thiobacilli). The pH, metals (Cd, Cr, Cu, Pb and Zn), and sulphate were used to evaluate system effectiveness. At an HRT of 8 days in each solubilization tank, and a S*-concentration of 4g l(-1), sulphur oxidation efficiency was about 70%, while the metal removals were: Cd 90%, Cr 93%, Cu 96%, Pb 67% and Zn 98%. The finished product (biosolids) met the OME requirements for metal concentrations for producing compost or fertilizer for unrestricted use. The reduction of pathogenic indicator organisms was demonstrated in earlier studies.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.227
Threshold uncertainty score0.463

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 head0.017
GPT teacher head0.223
Teacher spread0.205 · 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 designBench or experimental
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

Citations4
Published2006
Admission routes2
Has abstractyes

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