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Record W2125349119 · doi:10.1139/s05-015

Impact of compost amendments and operating temperature on diesel fuel bioremediation

2006· article· en· W2125349119 on OpenAlexaffvenue
Rafik M. Hesnawi, Daryl McCartney

Bibliographic record

VenueJournal of Environmental Engineering and Science · 2006
Typearticle
Languageen
FieldEnvironmental Science
TopicMicrobial bioremediation and biosurfactants
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsCompostBioremediationAmendmentPhenanthreneDiesel fuelMineralization (soil science)Environmental scienceGreen wasteMesophileChemistryPulp and paper industryWaste managementEnvironmental chemistryContaminationBiologyBacteriaEcologySoil waterSoil science

Abstract

fetched live from OpenAlex

A laboratory study was conducted to investigate the performance of composting bioremediation of a sand material contaminated with diesel fuel and radio-labeled phenanthrene. The material was amended with either fresh feedstock material or finished compost and then incubated at either thermophilic or mesophilic temperatures for 126 d. In controls that were not amended with compost, no mineralization of 14 C phenanthrene was detected, whereas treatments that received compost amendments had significant phenanthrene mineralization, ranging from 25% to 42% of initial concentrations. The lowest extractable diesel range organic residual (1092 mg kg –1 total solids) was observed in the treatment receiving fresh compost amendment and incubated at thermophilic temperatures, whereas the highest residual (8507 mg kg –1 total solids) was observed in the control without any amendment. Whereas all the treatments that received amendment dramatically outperformed the control reactors, significant differences were observed among the treatment performances. This suggests that amendment type and operating temperature were important factors impacting bioremediation performance. Key words: compost amendments, bioremediation, diesel fuel, phenanthrene, temperature.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.901
Threshold uncertainty score0.285

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 head0.003
GPT teacher head0.194
Teacher spread0.190 · 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 designObservational
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

Citations14
Published2006
Admission routes2
Has abstractyes

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