Bibliographic record
Abstract
This Bachelor thesis is focused on autism and the importance of an assistance dog for people with this handicap. Part of this work deals with pervasive developmental disorders, autism where the terms of the classification system ranks. There is also described three basic areas: social interaction, communication, interests and rituals. The work also indicated the distribution of pervasive developmental disorders according to the international classification system for children's autism, atypical autism, Asperger syndrome, other childhood disintegration disorder, Rett syndrome, other pervasive developmental disorders and autistic traits. The work also shows the development of disorders in different age groups and concise diagnosis of autism. The second part deals with general zootherapy and detail subsequently canistherapy. There is a treatise on the history and forms of animal assisted therapy. An important part of the dismantling of the interaction of humans and animals, the influence of animal assisted therapy on individuals with pervasive developmental disorder. The work is mention of contraindications and canistherapy of ethics for assistance dogs. There are also described breeds suitable for this activity, Labrador Retriever, Golden Retriever, Flat-Coated Retriever and a Border Collie. In the south part includes information on training assistance dog in various organizations in the Czech Republic, specifically at the training center Helppes and organizacion Pomocné tlapky.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.074 | 0.024 |
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".