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Record W2078341020 · doi:10.1080/09593330.2004.9619397

Comparison of Different Carbon Sources for Ground Water Denitrification

2004· article· en· W2078341020 on OpenAlexaff
M.-J. Lorrain, B. Tartakovsky, A. Peisajovich-Gilkstein

Bibliographic record

VenueEnvironmental Technology · 2004
Typearticle
Languageen
FieldEnvironmental Science
TopicWastewater Treatment and Nitrogen Removal
Canadian institutionsBiotechnology Research Institute
Fundersnot available
KeywordsDenitrificationNitrateChemistryCarbon fibersEnvironmental chemistryCarbon sourceSucroseTotal organic carbonEnvironmental engineeringNitrogenEnvironmental scienceFood scienceOrganic chemistryBiochemistryMaterials science

Abstract

fetched live from OpenAlex

This study presents a comparison of denitrification rates and denitrification stoichiometry when using different sources of carbon. Denitrification tests were carried out in test bottles containing water and soil samples acquired at a nitrate-contaminated site and supplemented with either sucrose, acetate or ethanol. The tests demonstrated nitrate removal in all carbon source supplemented bottles. The rate of denitrification and the required amount of a carbon source, however, depended on the choice of substrate. Ethanol and acetate were found to provide the highest denitrification rate, that of 1.4 - 1.6 mg-N l(-1) d(-1). Sucrose-supplemented bottles demonstrated a significantly slower denitrification rate, that of 0.6 - 0.9 mg-N l(-1) d(-1). In addition to slow denitrification rates, sucrose-supported denitrification required more carbon source.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.012
GPT teacher head0.232
Teacher spread0.220 · 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 source (direct Gemma or distilled Codex), 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

Citations20
Published2004
Admission routes1
Has abstractyes

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