Jämförelse mellan labradoodle, goldendoodle & deras föräldraraser : Är ”doodles” friskare, snällare och modigare än sina renrasiga föräldrar?
Bibliographic record
Abstract
The advantages with breeding purebred dogs are that offspring will be more predictable when it comes to morphology, possible diseases, and mentality. This enables breeding more healthy animals since it makes it possible to track the health history, but it is also resulting in a higher inbreeding, extreme breeding standards and thus sicker animals. Labradoodle and Goldendoodle are two new mix-breeds, bred to be the ultimate companion dog with a good mentality and health. In this literature review their physical and mental health is compared with their parent breeds, Labrador, or Golden retriever, and Poodle. The breed-specific health problem that is seen with the parent breeds is also seen in labradoodle and goldendoodle. Hybrid vigour is not present in doodles. Mix-breeds live longer lives, but they also seem to be less stable with higher fear and aggression
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.021 | 0.012 |
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; both teacher heads agree on what is shown here.
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".