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Record W2519004765 · doi:10.1002/ieam.1833

Flexing the PECs: Predicting environmental concentrations of veterinary drugs in Canadian agricultural soils

2016· article· en· W2519004765 on OpenAlexaffabout
Sigrun A. Kullik, Andrew M. Belknap

Bibliographic record

VenueIntegrated Environmental Assessment and Management · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicPharmaceutical and Antibiotic Environmental Impacts
Canadian institutionsImpactGlobal Affairs CanadaHealth Canada
Fundersnot available
KeywordsVeterinary drugVeterinary DrugsLivestockAnimal husbandryAgricultureEuropean unionVeterinary medicineAgency (philosophy)Agricultural scienceEnvironmental impact assessmentBusinessBiotechnologyMedicineEnvironmental scienceBiologyEcology

Abstract

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Abstract Veterinary drugs administered to food animals primarily enter ecosystems through the application of livestock waste to agricultural land. Although veterinary drugs are essential for protecting animal health, their entry into the environment may pose a risk for nontarget organisms. A means to predict environmental concentrations of new veterinary drug ingredients in soil is required to assess their environmental fate, distribution, and potential effects. The Canadian predicted environmental concentrations in soil (PECsoil) for new veterinary drug ingredients for use in intensively reared animals is based on the approach currently used by the European Medicines Agency for VICH Phase I environmental assessments. The calculation for the European Medicines Agency PECsoil can be adapted to account for regional animal husbandry and land use practices. Canadian agricultural practices for intensively reared cattle, pigs, and poultry differ substantially from those in the European Union. The development of PECsoil default values and livestock categories representative of typical Canadian animal production methods and nutrient management practices culminates several years of research and an extensive survey and analysis of the scientific literature, Canadian agricultural statistics, national and provincial management recommendations, veterinary product databases, and producers. A PECsoil can be used to rapidly identify new veterinary drugs intended for intensive livestock production that should undergo targeted ecotoxicity and fate testing. The Canadian PECsoil model is readily available, transparent, and requires minimal inputs to generate a screening level environmental assessment for veterinary drugs that can be refined if additional data are available. PECsoil values for a hypothetical veterinary drug dosage regimen are presented and discussed in an international context. Integr Environ Assess Manag 2017;13:331–341. © 2016 Her Majesty the Queen in Right of Canada. Integrated Environmental Assessment and Management published by Wiley Periodicals, Inc. on behalf of SETAC. Reproduced with the permission of the Minister of Health. Key Points Defaults and production classes that correspond to predominant Canadian agricultural practices have been developed for the PECsoil calculation for intensively reared livestock used in the European Union during VICH Phase I. The Canadian PECsoil model is readily available, transparent, and requires minimal inputs to generate a screening level environmental assessment for veterinary drugs that can be refined if additional data are available. PECsoil values for a hypothetical veterinary drug dosage regimen for cattle, pork, and poultry are presented and discussed in an international context. The Canadian PECsoil can serve as a template for adaptation of the EMA PECsoil model for VICH Phase I environmental assessment of new veterinary drugs in other regions.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.189
Threshold uncertainty score0.999

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)0.0020.000

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 head0.014
GPT teacher head0.265
Teacher spread0.251 · 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.

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

Citations4
Published2016
Admission routes2
Has abstractyes

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