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Record W1564732284 · doi:10.1080/20015891109428

Biodegradation of Diesel Fuel in Soil Under Various Nitrogen Addition Regimes

2001· article· en· W1564732284 on OpenAlexafffund
Timothy R. Brook, Warren Stiver, Richard G. Zytner

Bibliographic record

VenueSoil and Sediment Contamination An International Journal · 2001
Typearticle
Languageen
FieldEnvironmental Science
TopicMicrobial bioremediation and biosurfactants
Canadian institutionsUniversity of Guelph
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsChemistryEnvironmental chemistryNitrogenNitrateUreaBioremediationAmmonium sulfateDiesel fuelBiodegradationAmmoniumAmmonium nitrateNutrientPotassium sulfatePotassium nitrateSulfateContaminationPotassiumEcologyOrganic chemistry

Abstract

fetched live from OpenAlex

Abstract Bioremediation is a growing technology for treating fuel-contaminated soils. Many biological, physical, and chemical parameters control the rate and efficiency of this process, including type and concentration of contaminants, temperature, oxygen content, and nutrient status. This study investigated the effect that nitrogen sources and concentrations had on the degradation rate of diesel fuel in nutrient limited soil at two carbon-to-nitrogen ratios. The different sources of nitrogen studied were ammonium nitrate, ammonium sulfate, potassium nitrate, urea, and urea oligomers (control release fertilizer). Laboratory experiments were conducted on field-contaminated soil using sealed bioreactors at a controlled temperature of 25°C. For both carbon-to-nitrogen ratios tested, hydrocarbon degradation rates were the highest for the ammonium sulfate (20:1 at 0.032 d−1; 40:1 at 0.019d−1) and urea treatments (20:1 at 0.025 d−1; 40:1 at0.011 d−1). A degradation rate correlation as a function of nitrate and ammonia concentrations was developed. The correlation suggests the occurrence of nitrate inhibition at elevated nitrate concentrations. Keywords: bioremediationdiesel fuelnitrogen additionbioavailable nitrogensoil

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.206
Threshold uncertainty score0.999

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 communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.011
GPT teacher head0.237
Teacher spread0.226 · 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.

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

Citations49
Published2001
Admission routes2
Has abstractyes

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