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Record W4403260567 · doi:10.2965/jwet.24-047

Methanogens’ Death Induced by Sulphide and its Kinetic Modelling

2024· article· en· W4403260567 on OpenAlexaff
Oanh Thi Phung, Meng Sun, Mitsuharu Terashima, Rajeev Goel, Hidenari Yasui

Bibliographic record

VenueJournal of Water and Environment Technology · 2024
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicCassava research and cyanide
Canadian institutionsHydromantis Environmental Software Solutions (Canada)
FundersScience and Technology Research Partnership for Sustainable DevelopmentJapan Science and Technology AgencyJapan Society for the Promotion of ScienceJapan International Cooperation Agency
KeywordsEnvironmental scienceKinetic energyPhysics

Abstract

fetched live from OpenAlex

This study is aimed at investigating the poisoning effect of sulphide on methanogenic cultures. Using cultures enriched with either acetate or formate as a sole electron donor, sets of 1-week batch inhibition tests were performed to analyse the dynamic change of the living microorganism concentrations under the varied sulphide concentrations between zero and 400 mg-S L−1. In both cultures, the cellular decay was doubled when the cultures were placed in 100 mg-S L–1 of total sulphide. When the cultures were exposed to higher sulphide, higher specific decay rates were obtained. Because of the low correlations of the unionised sulphide concentrations to the specific decay rates, the total sulphide concentration was thought to be the dominant inhibition factor rather than its unionised form. To express the acceleration of cellular decay, a mathematical model was developed. Since the decay phenomena of both cultures were quite similar to each other, a culture-wide empirical formula was obtained to calculate the specific decay rate. In the model equation, the specific decay rate of the methanogenic cultures was linearly expressed with a coefficient of 0.30∙10−3 ± 0.05∙10−3 mg-S−1 L d−1 for sulphide concentration and a coefficient of 0.044 ± 0.013 d–1 for the inherent specific decay rate.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.092
Threshold uncertainty score0.103

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 head0.028
GPT teacher head0.220
Teacher spread0.192 · 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

Citations1
Published2024
Admission routes1
Has abstractyes

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