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Record W3200977382 · doi:10.9734/bpi/magees/v6/12854d

Investigating the Effect of Soil pH on Sorption of Salinomycin in Clay and Sandy Soils

2021· book-chapter· en· W3200977382 on OpenAlexaff
R. Jayashree, Shiv O. Prasher, Rajvinder Kaur, Ramanbhai M. Patel

Bibliographic record

VenueBook Publisher International (a part of SCIENCEDOMAIN International) · 2021
Typebook-chapter
Languageen
FieldEnvironmental Science
TopicPharmaceutical and Antibiotic Environmental Impacts
Canadian institutionsMcGill University
Fundersnot available
KeywordsSorptionSoil waterOrganic matterLoamSalinomycinEnvironmental chemistryChemistryDesorptionSoil organic matterSoil scienceEnvironmental scienceAdsorptionOrganic chemistry

Abstract

fetched live from OpenAlex

Salinomycin is a polyether ionophore that is often used to prevent coccidial infections and promote growth in poultry. A considerable portion of the antibiotic is excreted as the parent chemical, which eventually ends up in agricultural soils. This necessitates researchers conducting sorption-desorption studies to better understand the compound's behaviour in the soil environment. Salinomycin sorption was studied in four agricultural soils, including a clay soil with low organic matter content (LOM), a clay soil with high organic matter content (HOM), a sandy soil with HOM, and a loamy sandy (LOM) soil, at three pH levels: 4, 7, and 9. The batch equilibration approach was used to conduct the desorption investigations. All soils were shown to be severely sorbed by more than 98 percent salinomycin, regardless of soil organic matter level or pH. Salinomycin sorption to the sandy soil increased marginally as the pH decreased, while sorption to the two clay soils increased marginally as the pH increased. Salinomycin desorption in methanol was less than 0.2 percent of the amount administered after 72 hours; whereas, it was greater than 70% with phosphate buffer (pH 7). Salinomycin could offer major threats to both shallow ground water and surface water bodies, because the phosphate buffer would imitate, to some extent, the quality of water flowing through field soils containing various salts.

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)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.004
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.022
GPT teacher head0.270
Teacher spread0.248 · 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 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

Citations1
Published2021
Admission routes1
Has abstractyes

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Same venueBook Publisher International (a part of SCIENCEDOMAIN International)Same topicPharmaceutical and Antibiotic Environmental ImpactsFrench-language works237,207