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Record W2233944291 · doi:10.3382/japr/pfv067

Economic cost-benefit analysis of the use of bacitracin methylene disalicylate in broilers affected with necrotic enteritis

2015· article· en· W2233944291 on OpenAlexaff
Gloria Chan, Alessia Guthrie, Paul Sockett, Jeff Wilson, Robert A. Moody, Steven Clark

Bibliographic record

VenueThe Journal of Applied Poultry Research · 2015
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAnimal Nutrition and Physiology
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsFlockBacitracinBroilerMedicineAnimal scienceReturn on investmentVeterinary medicineProduction (economics)BiologyEconomicsAntibiotics

Abstract

fetched live from OpenAlex

The flock-level economic impact associated with necrotic enteritis (NE) in broiler production was estimated in flocks that were administered 55 ppm bacitracin compared to flocks that were not given prophylactic medication. Current production parameters and economic values available for the United States were used in a cost-benefit evaluation to determine the return on investment (ROI) in prophylactic use of BMD. Estimates were expressed in US dollars from the perspective of poultry producers. Costs to the producer, including feed and medication, were compared with benefits accrued from reduced mortality rates in treated flocks and improved weight gain. A sensitivity analysis evaluated the ROI from BMD use under varying mortality rates (one to 20%) and target weights (4.63 to 7.94 lb). The average flock-level ROI in BMD was $4.51, and sensitivity analyses demonstrated that the ROI ranged from $2.28 to 7.24 depending on NE morbidity rate and target weight. The average return in BMD per lb produced was c/1.5, and sensitivity analyses demonstrated that return ranged from c/0.8 to 2.1 depending on NE morbidity rate and target weight. These analyses demonstrated that a positive ROI can be realized in flocks at risk for NE through the prophylactic use of BMD in broiler production.

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)0.000
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 score0.950
Threshold uncertainty score0.145

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.165
GPT teacher head0.326
Teacher spread0.161 · 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

Citations2
Published2015
Admission routes1
Has abstractyes

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