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From Chemostat/Retentostat to Soil: Modeling bioavailability limitations on atrazine degradation

2020· article· en· W3188795045 on OpenAlexaff
Luciana Chávez Rodríguez, Holger Pagel, Thilo Streck, Brian Ingalls

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEnvironmental Science
TopicPesticide and Herbicide Environmental Studies
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsAtrazineChemostatBiodegradationEnvironmental chemistrySoil waterChemistryEnvironmental sciencePesticideBioavailabilityPesticide degradationDegradation (telecommunications)Microbial biodegradationEnvironmental engineeringMicroorganismSoil scienceAgronomyBacteriaBiology

Abstract

fetched live from OpenAlex

Atrazine has been banned in Europe since 2003, but is still a widely used herbicide in the rest of the world. It presents an environmental threat due to its environmental persistence and ecotoxicity. Although soil bacteria have evolved effective biodegradation pathways, atrazine persists in soils at low concentrations making soils to potential continuous sources of groundwater pollution. Experiments using isotopologues of atrazine in simplified systems (chemostat and retentostat) indicate, that limited mass transfer across the cell membrane controls atrazine degradation at low concentrations. We extended and parameterized an existing mathematical model of atrazine degradation in the chemostat/retentostat system using laboratory data. By integrating this modeling approach into a more complex soil model, the role of mass transfer across bacterial cell membranes can be assessed against other rate limiting processes of atrazine biodegradation in soil at low concentrations.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.036
Threshold uncertainty score0.072

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0020.001
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 head0.082
GPT teacher head0.245
Teacher spread0.163 · 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 designSimulation or modeling
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

Citations0
Published2020
Admission routes1
Has abstractyes

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