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Record W3087365414 · doi:10.1080/03601234.2020.1807263

Sulfamethoxazole sorption by cattail and switchgrass roots

2020· article· en· W3087365414 on OpenAlexafffund
Theresa Adesanya, Francis Zvomuya, Annemieke Farenhorst

Bibliographic record

VenueJournal of Environmental Science and Health Part B · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicPharmaceutical and Antibiotic Environmental Impacts
Canadian institutionsUniversity of Manitoba
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of Manitoba
KeywordsSorptionChemistryKineticsReaction rate constantEnvironmental chemistryAdsorptionOrganic chemistry

Abstract

fetched live from OpenAlex

Sorption to roots is one of several mechanisms by which plant-assisted attenuation of antibiotics can be achieved. The objectives of this study were to (1) evaluate the sorption of sulfamethoxazole (SMX) by cattail and switchgrass roots, (2) determine the kinetics of SMX sorption by cattail and switchgrass roots, and (3) characterize the temperature-dependency of SMX sorption. A batch sorption experiment was conducted to measure SMX sorption by roots of the two plant species using five initial antibiotic concentrations (2.5, 5, 10, 15, and 20 µg L−1) and eight sampling times (0, 0.5, 1, 2, 4, 8, 12, and 24 h). Another batch experiment was conducted at three temperatures (5, 15, and 25 °C) to determine the effect of temperature on sorption kinetics. SMX sorption followed pseudo-second-order kinetics. The pseudo-second-order rate constant (k2) decreased with increasing temperature for both plant species. The rate constant followed the order: 5 °C = 15 °C > 25 °C for cattail and 5 °C > 15 °C = 25 °C for switchgrass. Results from this study show that switchgrass roots are more effective than cattail roots in the removal of SMX. Therefore, the use of switchgrass in systems designed for phytoremediation of contaminants might also provide an efficient removal of some antibiotics.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.687
Threshold uncertainty score0.535

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
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 head0.049
GPT teacher head0.327
Teacher spread0.278 · 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

Citations7
Published2020
Admission routes2
Has abstractyes

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Same venueJournal of Environmental Science and Health Part BSame topicPharmaceutical and Antibiotic Environmental ImpactsFrench-language works237,207