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Record W3041129781 · doi:10.1163/15685306-bja10019

Effect of Tranquil and Active Video Representations of an Unfamiliar Dog on Subjective Mental States

2020· article· en· W3041129781 on OpenAlexaff
Natalie Ein, Maureen J. Reed, Kristin S. Vickers

Bibliographic record

VenueSociety and Animals · 2020
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicHuman-Animal Interaction Studies
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsAlertnessAnxietyPsychologyVideo tapeVideo recordingAudiologyMedicinePsychiatryMultimediaComputer science

Abstract

fetched live from OpenAlex

Abstract The aim of this pilot study was to examine the effects of different videos of an unfamiliar dog (tranquil and active) on subjective mental state measures. All participants watched two videos of an unfamiliar dog (tranquil and active). Subjective measures of stress, anxiety, alertness, attention, likeability, and cuteness were assessed. The results showed that the tranquil dog video significantly decreased anxiety only. Additionally, the active dog video significantly decreased stress and anxiety. Across the videos, the results showed the active dog video significantly improved subjective alertness and attention when compared with the tranquil dog video. Lastly, the active dog video was rated more likeable and cuter relative to the tranquil dog video. The practical implications of these findings could include how to improve various subjective mental states for humans in public settings (e.g., hospital) where nonhuman animals are not always allowed.

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)0.000
metaresearch head score (Gemma)0.004
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 score0.007
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.004
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)0.0070.000

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 head0.013
GPT teacher head0.352
Teacher spread0.338 · 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

Citations7
Published2020
Admission routes1
Has abstractyes

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