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Survey of Selected Risk Factors and Therapeutic Strategies for Parasitism on Milk Production Response of Lactating Dairy Cattle

2019· article· en· W318719037 on OpenAlexaff
K.E. Leslie, Amy Jackson, T.F. Duffield, Ian R. Dohoo, Luc DesCôteaux, Ernest Hovingh

Bibliographic record

VenueThe Bovine Practitioner · 2019
Typearticle
Languageen
FieldVeterinary
TopicHelminth infection and control
Canadian institutionsUniversity of Prince Edward IslandUniversité de MontréalUniversity of Guelph
Fundersnot available
KeywordsHerdProductivityDairy cattleMilk productionAgricultural scienceDairy industryLactationProduction (economics)BiologyAnimal husbandryBiotechnologyVeterinary medicineAnimal scienceEnvironmental healthBusinessMedicineFood sciencePregnancyAgricultureEconomicsEcology

Abstract

fetched live from OpenAlex


 A recent review of studies on the effect of parasite control programs on productivity of lactating dairy cattle has reported positive, but variable, results. A conjoint analysis survey was conducted, using dairy industry professionals and practicing veterinarians, to estimate the impact of various management strategies on the control of parasites on milk production in dairy herds. The results of the survey suggested positive associations of confinement housing systems, replacement heifer treatment programs and cow treatment programs with milk production. Spreading manure on pastures and not using a treatment program in heifers were determined to have the most important negative association with milk production. The survey responses also suggested that topical treatment for external parasites, and strategic use of endectocide products in the prepartum period, would have a positive effect on production. Areas of high priority for research on the association between parasites and productivity in dairy cattle would include the relative impact of whole herd versus strategic lactation cycle treatment programs.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.745
Threshold uncertainty score0.285

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 head0.055
GPT teacher head0.333
Teacher spread0.277 · 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

Citations3
Published2019
Admission routes1
Has abstractyes

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