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Record W2557599778 · doi:10.1108/ijes-07-2016-0012

Assessment of emergency/disaster preparedness and awareness for animal owners in Canada

2016· article· en· W2557599778 on OpenAlexaffabout
Mary Onukem

Bibliographic record

VenueInternational Journal of Emergency Services · 2016
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAgriculture and Farm Safety
Canadian institutionsRoyal Roads University
Fundersnot available
KeywordsPreparednessEmergency managementVulnerability (computing)BusinessDisaster preparednessOriginalityPublic relationsValue (mathematics)Emergency responseOrder (exchange)Medical emergencyPolitical scienceMedicineQualitative researchComputer securitySociologyFinanceComputer science

Abstract

fetched live from OpenAlex

Purpose The purpose of this paper is to review the vulnerability and challenges faced by Canadian pet owners in times of disaster and evaluate emergency preparedness measures put in place to address the identified issues. Design/methodology/approach Emergency preparedness strategies from different countries were identified to weigh against Canada’s state of preparedness. Findings Pet/animal owners without emergency plans for their animals are more vulnerable than non-pet owners when they need to flee from disaster; and as Canada faces disaster challenges, proactive preparedness in emergency demands awareness, cooperation and commitment from everyone –governments, corporations, community groups and individuals become a necessity. Originality/value Based on the identified need, the paper reviews strategies that engages pet owners in preparing for emergency in order to keep individuals and their communities safe. This paper will be beneficial to policy makers, researchers, health educators, scholars and emergency management professionals that read the journal.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.626
Threshold uncertainty score0.899

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)0.0000.000

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 head0.020
GPT teacher head0.291
Teacher spread0.271 · 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 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

Citations11
Published2016
Admission routes2
Has abstractyes

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