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Record W4400113521 · doi:10.5937/femesprumns24015h

Biosecurity measures on ruminant farms

2024· article· en· W4400113521 on OpenAlexfundno aff
S. Hristov, Branislav Stanković, Jože Starič, Dimitar Nakov, Jasna Prodanov-Radulović, Bojan Milovanović, Ilias Chantziaras, Alberto Allepuz

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAnimal Disease Management and Epidemiology
Canadian institutionsnot available
FundersGovernment of Canada
KeywordsBiosecurityWork (physics)BusinessSanitationPlan (archaeology)Operations managementEngineeringGeographyMedicine

Abstract

fetched live from OpenAlex

In many scientific papers, the term biosecurity measures (BSMs) are defined as the implementation of segregation, sanitation or management procedures specifically designed to reduce the likelihood of the introduction, establishment, survival or spread of a potential pathogen into, within or from a farm or geographical area. The main BSMs (general external and internal BSMs related to newly introduced animals, farm workers, family members, visitors and service providers, vehicles, tools and equipment, location of farms, water and feed, control programs, management practices, handling of raw materials, work procedures, training, plans and records), based on literature data, guides, instructions, recommendation codes and checklists, are presented in the paper. In addition to the BSMs mentioned, the importance of segregation, cleaning and disinfection is emphasized. The most important and effective part of biosecurity is to keep infected animals and contaminated material away from non-infected animals. Cleaning and disinfecting barns, vehicles and equipment, especially boots and clothing, is a very effective way to minimize the transmission of disease to or between animals. It is very important to implement BSMs as a long-standing and successful practice on farms to maintain animal health. These measures should be included in a comprehensive biosecurity plan, which is tailored to farms characteristics and needs that must be fully implemented. A biosecurity plan and the design and implementation of biosecurity programs should address how farmers handle animals, vehicles and human access to the farm, as well as animal health and work procedures. Key BSMs should be followed on an ongoing basis and, working with veterinarians, farmers themselves can play an important role in keeping animals and production as healthy as possible. It is important to regularly assess the implementation of BSMs using appropriate questionnaires, which can highlight deficiencies that should be addressed immediately.

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 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)0.003
metaresearch head score (Gemma)0.005
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.038
GPT teacher head0.248
Teacher spread0.210 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

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