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Record W2074752520 · doi:10.1080/03601234.2012.665658

Solid beef cattle manure application impacts on soil properties and 17β-estradiol fate in a clay loam soil

2012· article· en· W2074752520 on OpenAlexafffund
Emmanuelle Caron, Annemieke Farenhorst, Xiying Hao, Claudia Sheedy

Bibliographic record

VenueJournal of Environmental Science and Health Part B · 2012
Typearticle
Languageen
FieldEnvironmental Science
TopicPharmaceutical and Antibiotic Environmental Impacts
Canadian institutionsAgriculture and Agri-Food CanadaUniversity of Manitoba
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsManureLoamSorptionAnimal scienceMineralization (soil science)Beef cattleLivestockChemistrySoil waterAgronomyEnvironmental chemistryEnvironmental scienceBiologySoil scienceEcologyAdsorption

Abstract

fetched live from OpenAlex

Livestock manure applied to agricultural land is one of the ways natural steroid estrogens enter soils. To examine the impact of long-term solid beef cattle (Bos Taurus) manure on soil properties and 17β-estradiol sorption and mineralization, this study utilized a soil that had received beef cattle manure over 35 years. The 17β-estradiol was strongly sorbed and sorption significantly increased (P < 0.05) with increasing soil organic carbon content (SOC) and with an increasing annual rate of beef cattle manure. The 17β-estradiol mineralization half-life was significantly negatively correlated, and the total amount of 17β-estradiol mineralized at 90 days (MAX) was significantly positively correlated with 17β-estradiol sorption. The long-term rate of manure application had no significant effect on MAX, but the addition of fresh beef cattle manure in the laboratory resulted in significantly (P < 0.05) smaller MAX values. None of the treatments showed MAX values exceeding one-third of the 17β-estradiol applied.

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 score0.007
Threshold uncertainty score0.014

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 communication0.0010.000
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 head0.058
GPT teacher head0.338
Teacher spread0.281 · 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

Citations6
Published2012
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