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Record W4225276632 · doi:10.3389/fvets.2022.892998

Working Dog Service, Harmful Agent Exposure and Decontamination

2022· review· en· W4225276632 on OpenAlexaboutno aff
Carla Jarrett, Morgan Brathwaite, Robert M. Gogal, Steven D. Holladay

Bibliographic record

VenueFrontiers in Veterinary Science · 2022
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicHuman-Animal Interaction Studies
Canadian institutionsnot available
FundersU.S. Department of Defense
KeywordsPersonal protective equipmentLabrador RetrieverMedicineHuman decontaminationMedical emergencyBusinessSurgeryCoronavirus disease 2019 (COVID-19)Pathology

Abstract

fetched live from OpenAlex

Working dogs are widely used by service professionals and the military for diverse roles that include sentry, patrol, messenger, tracking, search and rescue, law enforcement, apprehension, as well as explosives and narcotics detection. The expected tasks performed are in many ways determined by the breed, which is customarily a German Shepherd, Dutch Shepherd, Golden Retriever, Border Collie, Labrador Retriever, Beagle, or Belgium Malinois. Working dogs may be subject to injury from dangerous work environments or harmful agent exposure. Personal protective equipment (PPE) has been developed for such dogs, but may impede performance of duties or be poorly tolerated. Canine-specific field-use ready decontamination techniques and kits are therefore needed for use on working dogs that have encountered a harmful agent exposure. This report briefly reviews the development of the military working dog and examines personal protective equipment and decontamination techniques for working dogs after exposure to harmful biologic or chemical agents.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.996
Threshold uncertainty score0.964

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
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 head0.070
GPT teacher head0.382
Teacher spread0.312 · 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
GenreReview

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
Published2022
Admission routes1
Has abstractyes

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