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Record W2083275698 · doi:10.1002/ieam.111

How to test nontarget effects of veterinary pharmaceutical residues in livestock dung in the field

2010· article· en· W2083275698 on OpenAlexaffabout
Ralf Jochmann, Wolf U. Blanckenhorn, Luc F. Bussière, Charles Eirkson, John Jensen, Ute Kryger, J. Lahr, Jean-Pierre Lumaret, Jörg Römbke, Keith Wardhaugh, Kevin D. Floate

Bibliographic record

VenueIntegrated Environmental Assessment and Management · 2010
Typearticle
Languageen
FieldVeterinary
TopicHelminth infection and control
Canadian institutionsAgriculture and Agri-Food Canada
FundersUmweltbundesamt
KeywordsContext (archaeology)LivestockOrganismLegislationStandardizationTest (biology)Veterinary medicineBiologyEnvironmental resource managementEcologyEnvironmental scienceComputer scienceMedicinePolitical science

Abstract

fetched live from OpenAlex

To register veterinary medicinal products (VMPs) as parasiticides on pastured animals, legislation in the European Union requires an environmental risk assessment to test the potential nontarget effects of fecal residues on dung-dwelling organisms. Products with adverse effects in single-species laboratory tests require further, higher-tier testing to assess the extent of these effects on entire communities of dung-dwelling organisms under more realistic field or semifield conditions. Currently, there are no documents specifically written to assist researchers in conducting higher-tier tests or to assist regulators in interpreting the results of such tests in an appropriate context. Here we provide such a document, written by members of the SETAC Advisory Group DOTTS (Dung Organism Toxicity Testing Standardization) with research experience on dung fauna in central and southern Europe, Canada, Australia, and South Africa. This document briefly reviews the organisms that make up the dung community and their role in dung degradation, identifies key considerations in the design and interpretation of experimental studies, and makes recommendations on how to proceed.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.570
Threshold uncertainty score0.536

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 studies0.0000.000
Scholarly communication0.0000.000
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 head0.018
GPT teacher head0.325
Teacher spread0.308 · 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

Citations25
Published2010
Admission routes2
Has abstractyes

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