MétaCan
Menu
Back to cohort
Record W928825692

Denitrification by a Heterotrophic Denitrifier with an Aid of Slowly Released Molasses

2010· article· en· W928825692 on OpenAlexaboutno aff
Byung-Sun Lee, Kyuyeon Lee, 최종학, Youngjin Kim, Kyoungphile Nam, Doyeon Shin

Bibliographic record

VenueThe Journal of Korean Society of Soil and Groundwater Environment · 2010
Typearticle
Languageen
FieldEnvironmental Science
TopicWastewater Treatment and Nitrogen Removal
Canadian institutionsnot available
Fundersnot available
KeywordsHeterotrophNitrateChemistryDenitrificationNitriteCellulosePseudomonasNitrate reductasePulp and paper industryBacteriaEnvironmental chemistryChromatographyNitrogenBiologyBiochemistryOrganic chemistry
DOInot available

Abstract

fetched live from OpenAlex

This study was conducted to determine the potential applicability of slowly released molasses (SRM) to treat nitratecontaminated groundwater. SRM was made by dispersing molasses in hydroxy propyl methyl cellulose-silicamicrocrystalline cellulose matrix. Column test indicated that SRM could continuously release molasses with slowly decreasing release rates of up to 65 hrs, up to 215 hrs, and up to 361 hrs. A batch test using an isolated indigenous heterotrophic denitrifier Pseudomonas sp. KY1 having nitrite reductase (nirK) and liquid molasses demonstrated that the bacterium decreased 100 mg-N/L of nitrate to less than 10 mg-N/L at the C/N ratio of 10/1 in 48 hours. In a Pseudomonas sp. KY1-attached Ottawa sand column which continuously received molasses from a SRM-containing reservoir, the bacterium successfully removed nitrate from 20 mg-N/L to 3 mg-N/L during the 361 hours of column operation. The results showed the possibility that SRM can be used as a reliable, longterm extra carbon source for indigenous heterotrophic denitrifiers.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

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 head0.007
GPT teacher head0.188
Teacher spread0.181 · 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

Citations4
Published2010
Admission routes1
Has abstractyes

Explore more

Same venueThe Journal of Korean Society of Soil and Groundwater EnvironmentSame topicWastewater Treatment and Nitrogen RemovalFrench-language works237,207