ESTIMATION OF GENETIC PARAMETERS FOR BEHAVIORAL ASSESSMENT SCORES IN LABRADOR RETRIEVERS, GERMAN SHEPHERD DOGS, AND GOLDEN RETRIEVERS
Bibliographic record
Abstract
Among working dogs, the role of a guide dog ranks as one of the most noble and useful occupations and thus was recognized early as a category of working dogs worthy of focused research.Behavior issues top the list of most common reasons for rejecting dogs from working as guides.The objective of this study was to estimate genetic parameters for each of the 101 questions and 12 subscale factors measured by the Canine Behavioral Assessment and Research Questionnaire (C-BARQ).The C-BARQ is a standardized questionnaire that contains seven behavioral categories: training and obedience, aggression, fear and anxiety, separation-related behavior, excitability, attachment and attention-seeking, and a miscellaneous category.These categories and questions allow the evaluator to describe any dog's behavior.For this study, questionnaire responses were obtained on 3,149 and 3,348 Labrador Retrievers (LR) from Guiding Eyes for the Blind (GEB) and 989 and 1,187 Labrador Retrievers, 608 and 692 Golden Retrievers (GR), and 966 and 1,348 German Shepherd Dogs (GSD) from The Seeing Eye, Inc.(TSE) at 6-and 12-months of age, respectively.The estimates of heritability and standard errors from TSE dogs indicate that there is much genetic variation that could be exploited in selection against "Familiar dog-directed aggression/fear" (0.27 ± 0.12) of GR at 6-months, "Chasing" (0.22 ± 0.10) of GR at 6-months, and "Nonsocial fear" (0.27 ± 0.09) of GR at 12-months or in selection for improved "Trainability" of LR (0.46 ± 0.07), GSD (0.47 ± 0.07), and GR (0.20 ± 0.08) at 12-months.In general, the remaining factors and most of the 101 questions were found to be lowly heritable (< 0.10).These estimates are useful to understand more about the nature of behavioral traits leading to the production of successful working guides.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 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".