Bibliographic record
Abstract
The bachelor thesis deals with the education and training of assistance and guide dogs, especially their pretraining. For this area are most often used breeds of retrievers, purposeful crosses and to a lesser extent also shepherd breeds - especially border collies. Approaches and methods that can be used when working with these dogs are presented. The actual observation focuses on the individual stages of the pre-training of future assistance and guide dogs and describes in detail the methodology of their training. A questionnaire survey was used to analyse the success of the pre-training of 35 dogs, including an assessment of their temperament. These dogs were monitored for sex, breed, origin (with or without pass of origin), success in completing training and reasons for removal from the program. The actual progress of the pre-training was monitored and recorded for four puppies between January 2022 and completion in March 2024. These were specifically a Border Collie, a Flat Coated Retriever, a Golden Retriever and a Goldendoodle. The Golden Retriever was the most used breed for assistance and guide dog training, while the Golden Retriever was also the breed with the highest number of dogs removed from training. Only 29 % of dogs successfully completed training and were handed over. The most common reasons for removal from training were inappropriate personality traits, heart and eye findings and radiographic findings on the musculoskeletal system. Of the four puppies studied, only the Flat Coated Retriever completed complete training as an assistance dog.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.067 | 0.019 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".