MétaCan
Menu
← Back to cohort
Record W629413290 · doi:10.2521/jswtb.48.89

A Kinetic Expression for the Growth and Decay of Nitrite Oxidising Bacteria

2012· article· en· W629413290 on OpenAlexaff
Bing Liu, Daisuke Naka, Ian Jarvis, Rajeev Goel, Hidenari Yasui

Bibliographic record

VenueJapanese Journal of Water Treatment Biology · 2012
Typearticle
Languageen
FieldEnvironmental Science
TopicWastewater Treatment and Nitrogen Removal
Canadian institutionsHydromantis Environmental Software Solutions (Canada)
FundersJapan Society for the Promotion of Science
KeywordsNitriteBacteriaKinetic energyExpression (computer science)ChemistryEnvironmental chemistryBiochemical engineeringThermodynamicsBiologyPhysicsEngineeringOrganic chemistryNitrateComputer scienceGeneticsClassical mechanics

Abstract

fetched live from OpenAlex

亜硝酸酸化細菌による亜硝酸塩の硝酸塩へ酸化は、高濃度の亜硝酸によって阻害されることが知られている。このことを定量的に表現するために、回分実験をもとに亜硝酸酸化細菌の増殖と阻害に関する動力学モデルを作成することにした。高濃度亜硝酸に急激に暴露された亜硝酸酸化細菌は、ショックによって一時的に酸素吸収速度(増殖速度)が低下した。これらの濃度が酸素吸収速度に与える影響は非拮抗阻害型あるいは拮抗阻害型のMonod式で示され、ショックからの回復には経過時間をパラメータとする項をMonod式に加えることでその応答を精度よく表現することができた。一方、亜硝酸が一定濃度以上でシステムに存在すると、亜硝酸酸化細菌は徐々に死滅(不活性化)することも長期的な培養実験によって明らかになった。これらのことから、亜硝酸酸化細菌の阻害は増殖段階と死滅段階でそれぞれ別のメカニズムによって起きると推定された。亜硝酸によって死滅が促進されることを表現するために、亜硝酸による毒性の闘値を定義し、死滅全体の反応を⑴通常の自己分解、⑵亜硝酸の毒性による失活、の2つの素プロセスの和と考えた動力学モデルを作成した。これによって、亜硝酸が125-2,000 mgN/lの幅広い条件における亜硝酸酸化の回分反応パターンを説明することができた。

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)0.001
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 score0.009
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.018
GPT teacher head0.243
Teacher spread0.226 · 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

Citations5
Published2012
Admission routes1
Has abstractyes

Explore more

Same venueJapanese Journal of Water Treatment Biology→Same topicWastewater Treatment and Nitrogen Removal→French-language works237,207→