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Record W2134803707 · doi:10.1897/04-657r.1

Dissipation kinetics and mobility of chlortetracycline, tylosin, and monensin in an agricultural soil in Northumberland County, Ontario, Canada

2006· article· en· W2134803707 on OpenAlexaffabout
Jules C. Carlson, Scott A. Mabury

Bibliographic record

VenueEnvironmental Toxicology and Chemistry · 2006
Typearticle
Languageen
FieldEnvironmental Science
TopicPharmaceutical and Antibiotic Environmental Impacts
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsTylosinManureMonensinChlortetracyclineLoamChemistrySoil waterPesticideEnvironmental chemistryAnimal scienceAntibioticsEnvironmental scienceAgronomyBiologySoil scienceBiochemistry

Abstract

fetched live from OpenAlex

A robust high-throughput method was refined to extract three growth-promoting antibiotics, tylosin (TYL), chlortetracycline (CTC), and monensin (MON), from soil. Analysis was performed by electrospray liquid chromatography tandem mass spectrometry. Soil dissipation rate studies were performed in a farm field soil for antibiotics applied with and without manure. Tylosin, CTC, and MON followed first-order dissipation kinetics with half-lives of 4.5, 24, and 3.3 d, respectively, with the addition of manure and 6.1, 21, and 3.8 d, respectively, without manure. Manure application significantly increased TYL dissipation rate, perhaps because of the introduced microbial flora, but had no significant effect on CTC or MON. Monensin dissipation half-life was found to be much shorter in the field study than in a controlled laboratory study, perhaps because of differences in microbial communities. The antimicrobials were not highly mobile. Chlortetracycline was the only antibiotic detected at 25 to 35 cm depth and only up to 2% of the initial concentration in a sandy loam soil. These antibiotics are therefore expected to degrade primarily in agricultural soils before moving to greater depths or to groundwater in significant concentrations in most agricultural systems.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.544
Threshold uncertainty score0.819

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 studies0.0000.001
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 head0.005
GPT teacher head0.205
Teacher spread0.200 · 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 designObservational
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

Citations145
Published2006
Admission routes2
Has abstractyes

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