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Record W3161631597 · doi:10.21423/bovine-vol47no1p7-14

Efficacy of tilmicosin for on-arrival treatment of bovine respiratory disease in backgrounded winter-placed feedlot calves

2013· article· en· W3161631597 on OpenAlexfundaboutno aff
Joyce Van Donkersgoed, J. K. Merrill

Bibliographic record

VenueThe Bovine Practitioner · 2013
Typearticle
Languageen
FieldImmunology and Microbiology
TopicMicrobial infections and disease research
Canadian institutionsnot available
FundersEli Lilly CanadaElanco Animal HealthEli Lilly and Company
KeywordsTilmicosinFeedlotBovine respiratory diseaseMedicineAnimal scienceVeterinary medicineBiologyAntibioticsImmunology

Abstract

fetched live from OpenAlex

A trial was conducted in a commercial feedlot in southern Alberta, Canada to evaluate the cost-effectiveness of metaphylactic treatment with tilmicosin for control of bovine respiratory disease. First-pull treatment rates for BRD (P=0.006) and arthritis (P=0.02) were significantly lower in calves administered tilmicosin on arrival compared to non-medicated controls. Calves treated with tilmicosin at arrival-processing had gained an additional 20 lb (9.1 kg) at terminal weight sort (P=0.0002), with higher average daily gain (P=0.0001) and lower dry matter conversion (P=0.008). The cost- benefit of tilmicosin metaphylaxis in these feedlot calves depended on the method of calculation. Based on reductions in BRD and arthritis treatment rates and reduced feed costs because of improved dry matter conversion, there was a net advantage of $3.41CAN/head for those calves given tilmicosin on arrival compared to non-medicated controls. Based on improved treatment rates and additional weight gain, the net advantage was $8.09CAN/head for those calves given tilmicosin compared to non-medicated controls. These economic calculations assumed performance benefits observed at terminal weight sort were retained until slaughter, approximately 30 to 40 days later.

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 categoriesInsufficient payload (model declined to judge)
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 score0.494
Threshold uncertainty score0.999

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)0.0020.000

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 head0.039
GPT teacher head0.320
Teacher spread0.281 · 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

Citations9
Published2013
Admission routes2
Has abstractyes

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