The analysis of the final scores of hunting tests for retriever breeds
Bibliographic record
Abstract
This thesis is concerned with six Retriever dog breeds whereas emphasis is put on their hunting origin. In the first part, it provides a comprehensive overview of the history of Retriever breeds genesis, beginnings of their breeding and a brief description of the actual breeding conditions in our country. In the second part of the thesis, the basics of the inheritability of working abilities not only with hunting dog breeds are explained and a comparison of various working ability heritability tests, including their results, is provided. The main part of the thesis deals with the actual analysis of the results of club hunting tests targeted at the disciplines typical for the given test type. Within my research I focused on an analysis of hunting tests of Retrievers while using data obtained from the websites of Breeders Club of Hunting Retrievers (Klub loveckých slídičů) and Retriever Club CZ. I primarily studied the effect of the dog breed and year onto the assessment of certain disciplines. By means of the statistical method Anova I came to the conclusion that none of the monitored parameters, i.e. dog breed and year, have any significant effect on the assessment. The only statistically significant effect may be observed with the curly coated retriever breed in the discipline of tracking two pieces of furred game where the average grade of this breed amounts to 1.00, whereas with other breeds it ranges from 3.68 to 3.90. As far as the effect of the year on the assessment is concerned, only the year 2008 and 2009 may be considered statically significant in the discipline of standstill at a standpoint where average grades reached 2.75 or more precisely 3.25.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".