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Record W4401722273 · doi:10.1080/0194262x.2024.2392092

Reviews of Science for Science Librarians: Companion Animal Welfare During Natural Disasters

2024· article· en· W4401722273 on OpenAlexaff
Selenay Aytaç, André J. Nault, Nancy Frye, Clara Tran, Michele M. Dornisch, Seamus Ross

Bibliographic record

VenueScience & Technology Libraries · 2024
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicHuman-Animal Interaction Studies
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsNatural disasterNatural scienceAnimal welfareLibrary scienceNatural (archaeology)SociologyPolitical scienceComputer scienceHistoryBiologyGeographyEcologyPhilosophyArchaeologyEpistemology

Abstract

fetched live from OpenAlex

The purpose of this study is to present the results of a review to explore published accounts of companion animal welfare in the context of natural. We conducted a literature search limited to cats and dogs due to their popularity as pets worldwide and identified 1124 articles from which 91 were selected for analysis. Findings indicate a notable absence of legislation or policies at the national, regional, and municipal levels to respond to the needs of companion animals and to respect the bond between humans and their companion animals. Our research findings underscore the importance for policymakers to actively prioritize understanding the relationship between individuals and their companion animals. This proactive approach serves as a crucial mechanism for safeguarding human well-being and fostering healthier, more equitable communities. Based on our analyses, we conclude that the development of healthier and more equitable communities requires the development of targeted interventions that aim to protect and assist at risk companion animal families.

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)0.011
metaresearch head score (Gemma)0.059
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.016
Threshold uncertainty score0.058

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.059
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0160.021
Science and technology studies0.0010.002
Scholarly communication0.0070.005
Open science0.0010.003
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0130.003

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 head0.016
GPT teacher head0.325
Teacher spread0.309 · 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 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

Citations1
Published2024
Admission routes1
Has abstractyes

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