Genetic analysis of results of a Swedish behavior test on German Shepherd Dogs and Labrador Retrievers1
Bibliographic record
Abstract
The objectives of this study were to estimate genetic parameters and the influence of systematic effects on behavior test results in dogs. Behavior test results on 1,813 Labrador Retrievers (LR) and 2,757 German Shepherd Dogs (GSD) were analyzed. The behavior test included observations on courage, defense drive, prey drive, nerve stability, temperament, cooperation, affability, and gun shyness. Sex and age influenced most of the traits, and seasons of birth and testing and litter size and composition influenced some of the traits. Apart from defense drive in GSD, and courage, nerve stability, hardness, and affability in LR, all traits were heritable, with heritabilities ranging from 0.14 for hardness to 0.38 for affability in GSD, and from 0.03 for affability to 0.56 for gun shyness in LR. Genetic correlations ranged from 1.00 (LR) and 0.95 (GSD) between courage and hardness to -0.01 (LR) and -0.03 (GSD) between gun shyness and defense drive. Most genetic correlations were positive. Correlations with cooperation were mainly negative, especially in GSD. Genetic correlations between courage and defense drive in LR (0.26) and GSD (0.80), between courage and prey drive in LR (0.27) and GSD (0.65), between affability and nerve stability in LR (0.09) and GSD (0.64), between affability and temperament in LR (-0.24) and GSD (0.39), and between cooperation and hardness in LR (0.28) and GSD (-0.67) were significantly different between the breeds. Genetic parameters for defense drive and cooperation in GSD and hardness and gun shyness in LR were genetically different between the sexes. Results of this study indicate that correction for systematic effects is essential when making selection decisions. Estimating breeding values would be a good solution, incorporating both correction for systematic effects and using all genetic links. Genetic parameters need to be estimated for each breed separately.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".