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Record W2980161295 · doi:10.1101/798231

Influence of selective treatment on <i>Haematobia irritans</i> infestation of untreated cattle

2019· preprint· en· W2980161295 on OpenAlexafffund
Cecilia Miraballes, Henrik Stryhn, Antônio Thadeu Medeiros de Barros, Martín Lucas, Luísa N. Domingues, Rodrigo Vedovato Ribeiro, Macarena Monge, Alvaro Fraga, Franklin Riet-Corrêa

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2019
Typepreprint
Languageen
FieldVeterinary
TopicHelminth infection and control
Canadian institutionsUniversity of Prince Edward Island
FundersGovernment of Canada
KeywordsHaematobia irritansDiazinonInfestationFrench hornHerdVeterinary medicineBiologyPopulationAnimal scienceToxicologyMedicinePesticideMuscidaeAgronomyStomoxysEcology

Abstract

fetched live from OpenAlex

Abstract To reduce the use of insecticide treatments against Haematobia irritans we evaluated the impact of treating 15% of the bovines, with the greatest number of flies including bulls, with 40% diazinon ear tags, on the infestation of untreated cows. Horn fly susceptibility to diazinon was measured before and after treatment, and peaks of infestation were recorded. Three groups of Bradford bovines were evaluated: Group 1 (control untreated), Group 2 (15% treated) and Group 3 (control 100% treated). Weekly counts of horn flies were performed on the same animals for 78 days. Two peaks of infestation were recorded, and a higher number of horn flies occurred in the untreated control group than in the untreated cows of the selectively treated group throughout the entire period of the study, except for a single week. The horn fly field population was significantly more susceptible to diazinon than the reference susceptible strain both before and after insecticide treatment. In conclusion, treatment of 15% of the most infested animals from a herd, with 40% diazinon ear tags, quickly reduced horn fly infestations of the entire herd and may be a practical approach for horn fly control, reducing costs and chemical use.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.285
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
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 head0.020
GPT teacher head0.262
Teacher spread0.242 · 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 designBench or experimental
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

Citations0
Published2019
Admission routes2
Has abstractyes

Explore more

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