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Record W33392915 · doi:10.1007/s10071-020-01460-6

Program Verification and Programming Methodology.

2005· article· en· W33392915 on OpenAlexfundno aff
K. Rustan M. Leino

Bibliographic record

VenueAnimal Cognition · 2005
Typearticle
Languageen
FieldComputer Science
TopicEmbedded Systems Design Techniques
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsComputer scienceProgramming language

Abstract

fetched live from OpenAlex

Humans and dogs have co-evolved for over 10,000 years. Recent research suggests that, through the domestication process, dogs have become proficient at responding to human commands, attention and emotional states. However, the extent to which a companion dog responds to human emotions, such as stress, remains to be understood. This study examines whether a companion dog's stress, as measured by cortisol levels and heart rate, increases during a familiar outdoor walk in response to its owner's experience of stress. Sixty-eight owner/dog dyads participated in this study. The dyads were randomly assigned to an Experimental or Control group. Owners in the Experimental group were informed the walk would be digitally recorded for subsequent evaluation of their handling skills, whereas those in the Control group were informed the walk would be digitally recorded for archival purposes (no evaluation). This manipulation was implemented to induce a mild stress response in the owners. Salivary cortisol samples were collected from the owner and their dog before and after the walk. The dyad was also fitted with monitoring devices to record heart rate throughout the walk. Finally, personality information regarding the owner and their dog was collected. We found that cortisol production within the dyad showed a marginal inverse correlation. We also found that owners' Openness to Experience and dogs' Fearfulness influenced the heart rate of the other during the first minute of a walk. These results support that although stress may be detected within a dyad, this does not result in an associated significant change in cortisol or heart rate.

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)0.001
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: Other design · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.939
Threshold uncertainty score0.344

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.091
GPT teacher head0.352
Teacher spread0.261 · 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 designOther design
Domainnot available
GenreMethods

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
Published2005
Admission routes1
Has abstractyes

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