Výcvik psů a srovnání úspěšnosti jednotlivých plemen retrívrů ve výcviku
Bibliographic record
Abstract
This bachelor’s thesis focuses on retriever breeds, their description, use and training. I mention various theories about the process of dog domestication. I dealt with the issue of determining the dog's ancestry, who is now considered to be the common wolf (Canis lupus). I describe principles of dog learning and training methods that are most widely used today. Most commonly used are so-called positive methods like shaping, targeting and clicker training. I also mention the disciplines and working test evaluation system. At the end of the thesis I evaluated the success of every retriever breed in working test competitions organized by the civic association Retriever Sport CZ, who is eligible for organizing working test competitions. My evaluation is based on the percentage of dogs starting the competition, who then succeeded, or failed in individual categories. During the data processing, I discovered that more than 60% of participating dogs were Labrador Retrievers. According to the evaluation, this breed was the most successful in competition, as it most often won first place throughout the working tests.
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 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.073 | 0.029 |
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".