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Record W7103575623

Towards an improved estimate of antimicrobial use in animals: Adjusting the âpopulation correction unitâ calculation

2017· article· en· W7103575623 on OpenAlexaboutno aff

Bibliographic record

VenuePubMed Central · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicPharmaceutical and Antibiotic Environmental Impacts
Canadian institutionsnot available
Fundersnot available
KeywordsBiomass (ecology)PopulationUnit (ring theory)AntimicrobialNational standardAnimal species
DOInot available

Abstract

fetched live from OpenAlex

International comparisons of animal antimicrobial use (AMU) have typically been based on total national estimates of antimicrobials sales standardized by the national animal biomass calculated as the population correction unit (PCU). The objective of this paper was to compare the currently accepted PCU calculation with that of the adjusted population correction unit (APCU), which re-evaluates the standard animal weights used in the calculation and accounts for animal lifespan. The APCU calculation resulted in substantial changes to the 2009 national biomass estimates for cattle, pigs, and poultry in 8 European countries and Canada. The estimated national biomass for cattle increased 35% to 43%, while the estimated national biomass of pigs and poultry typically decreased by approximately 51% and 87%, respectively. Among the 9 countries, the total national APCU ranged from an increase of 1% to a decrease of 40% relative to PCU, and these differences were statistically significant. Adjusted population correction unit is preferred over PCU in comparing and contrasting AMU among animals with different lifespans because it is more transparently derived and is a reasonable approximation of the animal biomass at risk of antimicrobial treatment.

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 score0.093
Threshold uncertainty score0.662

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 communication0.0000.001
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 head0.051
GPT teacher head0.301
Teacher spread0.250 · 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

Citations0
Published2017
Admission routes1
Has abstractyes

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