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Record W2136078548 · doi:10.1080/15226510802655971

TECHNICAL NOTE: NITROGEN FERTILIZATION EFFECTS ON THE DEGRADATION OF AGED DIESEL OIL IN COMPOSTED DRILLING WASTES

2009· article· en· W2136078548 on OpenAlexafffund
Woo‐Jung Choi, Scott X. Chang

Bibliographic record

VenueInternational Journal of Phytoremediation · 2009
Typearticle
Languageen
FieldEnvironmental Science
TopicLandfill Environmental Impact Studies
Canadian institutionsUniversity of Alberta
FundersKillam Trusts
KeywordsCompostTotal petroleum hydrocarbonChemistryDegradation (telecommunications)Human fertilizationHydrocarbonDecompositionAgronomyBiodegradationPolycyclic aromatic hydrocarbonEnvironmental chemistrySoil contaminationSoil waterAnimal scienceEnvironmental scienceSoil scienceBiology

Abstract

fetched live from OpenAlex

Hydrocarbon-contaminated wastes generated from oil and gas drilling activities may be used as a soil amendment once composted and further decomposition of residual hydrocarbons can be accomplished after the composts are applied to soils. To test if N fertilization may enhance hydrocarbon decomposition, we investigated the effects of N application on hydrocarbon degradation in different-aged composts (1-, 2-, 3-, and 4-year-old composts, coded as 1Y, 2Y, 3Y, and 4Y composts, respectively) through a pot experiment planted with white spruce (Picea glauca [Moench] Voss) seedlings. The percentage degradation of total petroleum hydrocarbon (TPH, C11 to C40) in the composts without N fertilization was correlated to initial NH4+ concentrations (R = 0.99, P < 0.001). The percentage degradation of TPH was highest in the 3Y compost (41.1%) that had an initial level of 325.3 mg NH4+ -N kg(-1) and the lowest in the IY compost (9.3%) that had an initial level of 8.3 mg NH4+ -N kg(-1). The degradation of TPH was enhanced by Nfertilization in the 1Y (from 9.3 to 15.3%) and 4Y composts (from 14.3 to 22.6%) that had low initial NH4+ concentrations. Our results show that application of NH4+ -based fertilizers may enhance the degradation of TPH when initial NH4+ concentrations in the compost are low.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.540
Threshold uncertainty score0.283

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 head0.010
GPT teacher head0.252
Teacher spread0.242 · 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

Citations7
Published2009
Admission routes2
Has abstractyes

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