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Record W6999021793

Cães de assistência em Portugal : cães-guia, cães para surdos e cães de serviço

2013· dissertation· pt· W6999021793 on OpenAlexaboutno aff

Bibliographic record

VenueUniversity of Lisbon Repository (University of Lisbon) · 2013
Typedissertation
Languagept
FieldSocial Sciences
TopicAnimal Law and Welfare
Canadian institutionsnot available
Fundersnot available
KeywordsGovernment (linguistics)Context (archaeology)Focus (optics)Subject (documents)Field (mathematics)
DOInot available

Abstract

fetched live from OpenAlex

Assistance dogs in Portugal -guide dogs, hearing dogs and service dogsTodays society increasingly tries to provide a way to ease and improve the quality of the human being life.Assistance dogs is one of them.Guide dogs help blind people, hearing dogs assist people with severe hearing difficulties and service dogs help people with mobility, organic or mental problems.This type of dogs undergo a learning process using a specialized training in accordance with the type of users.Positive reinforcement and clicker techniques are the most used to achieve those aims.Moreover, breed and temperament choice is of utmost importance as the safety of people with disabilities is at stake.The main objective of this study was to characterize assistance dogs in Portugal in order to achieve that goal, two types of questionnaires were made, the first one to associations that train assistance dogs, and the another one to assistance dogs users.Inquires were made to three associations and 32 users throughout the country.It was found that Labrador Retriever is the most used breed due to its relaxed personality, its memory and its physical ability.The importance of neutering/spaying and vaccination was also accessed for all dogs.Finally we found that dog users feel more confident, secure, with better self-esteem and manage to socialize easier with society in general, after these animals were envolved in their lifes.In our days, law"s allow these type of dogs to accompany their users in any public place, whether transport or commercial facilities, therefore enabling establishment of very strong connection with their owners since they share almost all their daily tasks.

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 categoriesMeta-epidemiology (narrow), Science and technology studies, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.119
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.001
Science and technology studies0.0030.001
Scholarly communication0.0000.002
Open science0.0030.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.227
Teacher spread0.211 · 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.

Study designQualitative
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

Citations0
Published2013
Admission routes1
Has abstractyes

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