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Record W2281318612

Determining the type of nervous system in young puppies, regarding their proper education, socializing, and future behavior formation.

2010· article· en· W2281318612 on OpenAlexaboutno aff
K. Uzunova, V. Radev, I. Vashin, Y. Mitev, D. Kostov, V. Semerdjiev, D. Vladova

Bibliographic record

VenueTrakia Journal of Sciences · 2010
Typearticle
Languageen
FieldVeterinary
TopicVeterinary Orthopedics and Neurology
Canadian institutionsnot available
Fundersnot available
KeywordsGermanTest (biology)Set (abstract data type)Developmental psychologyPsychologyOrder (exchange)Computer scienceBiologyEcologyGeographyBusiness
DOInot available

Abstract

fetched live from OpenAlex

We studied the behaviour of 24 young puppies of the Labrador, Boxer and German Shepherd breeds, by using a bait test, in order to determine the type of nervous system, and depending on that to apply the most appropriate training, socializing, and behaviour formation. We proved that the used test was quick, easy to perform, convenient to set up, and not stressful for the subjects as it complied with the requirements for humane treatment of animals. According to our results, Labradors were most courageous and stable, followed by the Boxers and the German Shepherds.

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)0.000
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.066
GPT teacher head0.340
Teacher spread0.274 · 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

Citations1
Published2010
Admission routes1
Has abstractyes

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Same venueTrakia Journal of SciencesSame topicVeterinary Orthopedics and NeurologyFrench-language works237,207