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Record W2130018899 · doi:10.1139/l09-065

Protocols for the analysis of transformer oil and its degradation in soil by hydrogen peroxideA paper submitted to the Journal of Environmental Engineering and Science.

2009· article· en· W2130018899 on OpenAlexafffundvenue
Yanjun Chang, Gopal Achari, Cooper H. Langford

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2009
Typearticle
Languageen
FieldEnvironmental Science
TopicWater Quality Monitoring and Analysis
Canadian institutionsUniversity of Calgary
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsTransformer oilHydrogen peroxideTransformerHydrocarbonChemistryGas chromatographyDegradation (telecommunications)HydrogenPetroleumEnvironmental scienceEnvironmental chemistryChromatographyOrganic chemistryEngineeringElectrical engineering

Abstract

fetched live from OpenAlex

Conventional gas chromatography (GC) analysis of hydrocarbons displaying one broad peak provides limited information. Two GC analytical protocols using two-peak and three-peak approaches were developed to investigate transformer oil components and their degradation in soil by hydrogen peroxide. The two-peak method revealed transformer oil to be composed of 27 wt.% F2 fraction and 73 wt.% F3 fraction hydrocarbons. The three-peak method segregated the transformer oil into 40 wt.% light, 40 wt.% medium, and 20 wt.% heavy hydrocarbon fractions. In contrast to reports of success with several classes of lighter hydrocarbons, only limited degradations of transformer oil in soils by 15% and 30% hydrogen peroxide treatment was indicated by conventional one-peak analysis; two-peak analysis showed varying F2 and F3 fraction degradations with F2 fraction degradation being higher; three-peak analysis offered a richer understanding of the relative degradations of light, medium, and heavy fractions. The effect of a low pH environment on the degradation of transformer oil by hydrogen peroxide was evaluated using the three-peak method. It emerged that the degradation of the medium and heavier fractions of transformer oil was higher at pH 2.0.

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)0.001
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 score0.531
Threshold uncertainty score0.257

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.008
GPT teacher head0.206
Teacher spread0.198 · 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

Citations2
Published2009
Admission routes3
Has abstractyes

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