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Record W2281097465

Endectocide use in cattle and fecal residues: environmental effects in Canada.

2006· article· en· W2281097465 on OpenAlexaffabout
Kevin D. Floate

Bibliographic record

VenuePubMed · 2006
Typearticle
Languageen
FieldVeterinary
TopicHelminth infection and control
Canadian institutionsAgriculture and Agri-Food CanadaUniversity of Lethbridge
Fundersnot available
KeywordsMoxidectinDoramectinLivestockContext (archaeology)BiologyIvermectinFecesToxicologyEcology
DOInot available

Abstract

fetched live from OpenAlex

Endectocides, or macrocyclic lactones, are veterinary parasiticides used globally to control nematodes and arthropods affecting livestock. Cattle treated with these products fecally excrete residues that are toxic to dung-inhabiting insects, including species that accelerate dung degradation. Concerns have been raised that use of endectocides may reduce insect diversity and cause the accumulation of undegraded dung on pastures. This article synthesizes the results of studies performed to assess the nontarget effects of endectocide use in Canada. Residues reduce insect activity in dung of treated cattle for weeks to months after application. The duration of effect is influenced by several factors, including insect species and product. For example, in terms of toxicity, doramectin > ivermectin approximately equal to eprinomectin >> moxidectin. Reduced insect activity may retard dung degradation. Within the framework of regional conditions and management practices, endectocide use in Canada is unlikely to pose a significant widespread threat to the environment. Nevertheless, nontarget effects may be of concern to individual cow-calf operators, particularly those treating cattle in the spring. This synthesis, the first assessment of the nontarget effects of endectocide use in Canada, emphasizes the importance of presenting findings within an appropriate context.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.131
Threshold uncertainty score0.372

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 head0.017
GPT teacher head0.206
Teacher spread0.189 · 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

Citations60
Published2006
Admission routes2
Has abstractyes

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