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Record W3126247609 · doi:10.1016/j.vetmic.2021.109006

Genomics accurately predicts antimicrobial resistance in Staphylococcus pseudintermedius collected as part of Vet-LIRN resistance monitoring

2021· article· en· W3126247609 on OpenAlexaff
Gregory H. Tyson, Olgica Cerić, Jake Guag, Sarah M. Nemser, Stacey Borenstein, Ðurđa Slavić, Sarah J. Lippert, Rebecca McDowell, Aparna Krishnamurthy, Shannon Korosec, Cheryl Friday, Neil Pople, Matthew E. Saab, Julie‐Hélène Fairbrother, Isabelle Janelle, Deanna McMillan, Yugendar R. Bommineni, David Simon, Shipra Mohan, Susan Sánchez, Ashley Phillips, Paula Bartlett, Hemant Naikare, Cynthia K. Watson, Orhan Şahin, Chloe C. Stinman, Leyi Wang, Carol W. Maddox, Vanessa DeShambo, G. Kenitra Hendrix, Debra Lubelski, Amy Burklund, Brian V. Lubbers, Debbie Reed, Tracie M. Jenkins, Erdal Erol, Mukeshbhai Patel, Stephan Locke, Jordan Fortner, Laura Peak, Udeni B. R. Balasuriya, Rinosh Mani, Niesa Kettler, Karen Ege Olsen, Shuping Zhang, Zhenyu Shen, Martha Pulido Landinez, Jay Kay Thornton, Anil Thachil, Melissa Byrd, Megan E. Jacob, Darlene F. Krogh, Brett T. Webb, Lynn Schaan, Amar Patil, Sarmila Dasgupta, Shannon Mann, Laura B. Goodman, Rebecca Franklin‐Guild, Renee Anderson, Patrick K. Mitchell, Brittany D. Cronk, Missy Aprea, Jing Cui, Dominika A. Jurkovic, Melanie Prarat, Yan Zhang, Katherine Shiplett, Dubraska Diaz Campos, Joany Van Balen Rubio, Akhilesh Ramanchandran, Scott Talent, Deepanker Tewari, Nagaraja Thirumalapura, Donna Kelly, Denise Barnhart, Lacey Hall, Shelley C. Rankin, Jaclyn Dietrich, Stephen D. Cole, Joy Scaria, Linto Antony, Sara D. Lawhon, Jing Wu, Christine McCoy, Kelly R. Dietz, Rebecca Wolking, Trevor L. Alexander, Claire R. Burbick, Renate Reimschuessel

Bibliographic record

VenueVeterinary Microbiology · 2021
Typearticle
Languageen
FieldMedicine
TopicAntimicrobial Resistance in Staphylococcus
Canadian institutionsShared HealthCanadian Veterinary Medical AssociationUniversity of Prince Edward IslandAgriculture Food and Rural DevelopmentUniversity of SaskatchewanUniversity of Guelph
FundersU.S. Food and Drug AdministrationNational Institutes of Health
KeywordsStaphylococcus pseudintermediusBiologyAntibiotic resistanceResistomeBroth microdilutionAntimicrobialStaphylococcus aureusrpoBSalmonella entericaMicrobiologyGenotypeSalmonellaDrug resistanceStaphylococcusGeneticsGeneAntibioticsMinimum inhibitory concentrationBacteria

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.006
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.041
GPT teacher head0.306
Teacher spread0.265 · 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 source (direct Gemma or distilled Codex), 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

Citations33
Published2021
Admission routes1
Has abstractno

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