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Record W31443836 · doi:10.1016/j.jevs.2019.06.004

Gazetteer of Markets and Fairs to 1516: Cornwall

2003· article· en· W31443836 on OpenAlexfundno aff
Samantha Letters, Olwen Myhill

Bibliographic record

VenueJournal of Equine Veterinary Science · 2003
Typearticle
Languageen
FieldArts and Humanities
TopicScottish History and National Identity
Canadian institutionsnot available
FundersAtlantic Veterinary College
KeywordsGeographyHistoryAdvertisingBusiness

Abstract

fetched live from OpenAlex

Alcohol-based antisepsis has been extensively studied in human health care, but only little information is available regarding efficacy and tolerance in other species. The purpose of this study was to determine if an alcohol-based antiseptic is effective at reducing bacterial counts on equine skin and the appropriate contact time to do so, without causing any adverse skin reactions. Samples were collected before and after preparation from clipped sites over both jugular veins of horses and were plated on 3M Petrifilm Aerobic Count Plates in duplicate. Trial 1 tested an alcohol-based product (ET-80% ethanol) against a control of sterile saline at a contact time of 180-second. Trial 2 tested two different contact times of ET-90 and 180 seconds. All samples were assessed for colony-forming unit counts using an automated 3M Petrifilm reader. Data were analyzed by Kruskal-Wallis test, and the significance was set at P < .05. The results determined that ET had a mean 2.95 log<sub>10</sub> reduction from prepreparation to postpreparation colony-forming unit counts. A significant difference in log reduction between ET and control (P = .0033) was observed. There was no difference in log<sub>10</sub> reduction between the two contact times (P = .75). Mild urticaria was the only skin reaction observed and was often present in both ET and control groups. These findings demonstrate that ET is effective at reducing bacterial counts on equine skin at a contact time of 90 seconds without producing significant adverse skin reaction.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.920
Threshold uncertainty score0.919

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.047
GPT teacher head0.269
Teacher spread0.222 · 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 designNot applicable
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
Published2003
Admission routes1
Has abstractyes

Explore more

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