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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 OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

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.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesScience and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.083
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.004
Science and technology studies0.0010.024
Scholarly communication0.0000.000
Open science0.0020.001
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 head0.016
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