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Record W4399360276 · doi:10.1016/j.envpol.2024.124333

The fate of 17β-estradiol in snowmelt from a field with a history of manure application: A laboratory simulation and field study

2024· article· en· W4399360276 on OpenAlexafffund
Haven S.J.S. Soto, Inoka Amarakoon, Nora J. Casson, Darshani Kumaragamage, Henry F. Wilson

Bibliographic record

VenueEnvironmental Pollution · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicPharmaceutical and Antibiotic Environmental Impacts
Canadian institutionsAgriculture and Agri-Food CanadaUniversity of WinnipegUniversity of Manitoba
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsSnowmeltManureEnvironmental scienceSurface runoffHydrology (agriculture)Surface waterEnvironmental chemistryEnvironmental engineeringAgronomyChemistryEcologyGeologyGeotechnical engineeringBiology

Abstract

fetched live from OpenAlex

17β-estradiol is a naturally occurring estrogen, and livestock manure applied to agricultural fields is a major source to the environment. Liquid swine manure is widely applied to agricultural fields in the Canadian Prairies, a region where the majority of the annual runoff occurs during a brief snowmelt period over frozen soil. Transport of estrogens from manure amendments to soil during this important hydrological period is not well understood but is critical to mitigating the snowmelt-driven offsite transport of estrogens. This study quantified the concentration and load of 17β-estradiol in snowmelt from an agricultural field with a history of manure application under manure application methods: no manure applied, manure applied on the sub-surface, and on the surface, using a laboratory simulation study with flooded intact soil cores and a field study during snowmelt. A higher concentration of 17β-estradiol was in the laboratory simulation than in the field (mean laboratory pore water = 1.65 ± 1.2 μg/L; mean laboratory flood water = 0.488 ± 0.58 μg/L; and mean field snowmelt = 0.0619 ± 0.048 μg/L). There were no significant differences among manure application methods for 17β-estradiol concentration. Laboratory pore water concentrations significantly increased over time, corresponding with changes in pH. In contrast, there was no significant change in the field snowmelt concentrations of 17β-estradiol over time. However, for both laboratory simulation experiments and field-based snowmelt experiments, mean concentrations of 17β-estradiol were higher with subsurface than surface-applied manure, and the cumulative load of 17β-estradiol was significantly higher in the sub-surface than in surface applied. The mean cumulative load from the field study across all treatments (6.91 ± 3.7 ng/m2) approximates the magnitude of 17β-estradiol that could be mobilized from manured fields. The sub-surface application of manure seems to increase the persistence of 17β-estradiol in soil, thus enhancing the potential loss to snowmelt runoff.

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 score0.031
Threshold uncertainty score0.062

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0010.000
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 head0.010
GPT teacher head0.250
Teacher spread0.240 · 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

Citations3
Published2024
Admission routes2
Has abstractno

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