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Record W2023157389 · doi:10.1139/s03-043

Sodium azide interference in chemical and biological testing

2003· article· en· W2023157389 on OpenAlexvenueno aff
Ramesh Goel, Adrienne T. Cooper, Joseph R.V. Flora

Bibliographic record

VenueJournal of Environmental Engineering and Science · 2003
Typearticle
Languageen
FieldEnvironmental Science
TopicWater Treatment and Disinfection
Canadian institutionsnot available
Fundersnot available
KeywordsSodium azideAzideChemistryChlorineSodium nitriteNaphthaleneSodiumSodium nitrateInorganic chemistryNitriteNuclear chemistryNitrateOrganic chemistry

Abstract

fetched live from OpenAlex

This paper discusses the results of sodium azide interference encountered during lab-scale studies in which sodium azide was used as a microbial growth inhibitor. In separate tests to evaluate the oxidation of naphthalene by various oxidants, the disappearance of naphthalene in batch experiments containing chlorine was negligible after 12 d of incubation. However, 38% and 77% disappearance of naphthalene was observed when 11 mg/L of sodium azide was present in solutions containing 19.5 mg/L and 52.5 mg/L total chlorine, respectively. Chlorine was consumed faster in the batch studies containing sodium azide. Increasing levels of azide in solution also resulted in lower naphthalene and chlorine levels. In a separate set of experiments, sodium azide interfered with nitrate quantification and produced nitrate levels that were progressively lower with increasing azide concentrations. The decrease in nitrate was caused by reactions of sodium azide with nitrite formed during cadmium reduction. In both cases, sodium azide was added to the test solution either to prevent microbial growth or to inhibit existing microbial activity. These tests demonstrate that azide could potentially interfere in chemical and biological testing. Key words: sodium azide, inhibition, interference.

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 score0.560
Threshold uncertainty score0.229

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 head0.011
GPT teacher head0.183
Teacher spread0.172 · 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

Citations11
Published2003
Admission routes1
Has abstractyes

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