Influence of environmental factors and breed on the adaptive intelligence of dogs.
Bibliographic record
Abstract
This study aimed at evaluating the adaptive intelligence of different dog breeds, as well as that of multibreed hybrids, taking into account their sex, age and maintenance. 174 dogs were included in this study (81 bitches and 93 male dogs above 12 months of age). Among the studied dogs, 93 were hybrids and 81 were pedigree dogs, including the following 10 breeds: Rottweiler; German Shepherd; Boxer, Dachshund; spaniels (English Cocker Spaniel and Springer Spaniel); Labrador Retriever; pointers (Irish Setter, English Setter, Scottish Setter, Wire-haired pointer and Short-haired pointer); Border Collie; pinscher; and schnauzers (miniature and giant). Three age groups were distinguished: I - below 3 years of age; II - 3.5 to 8 years of age; and III - above 8 years of age. All the dogs were divided with reference to the maintenance system maintained in a pen (pen), block of flats (flat) and around the house (house). The experimental dogs were also divided into 3 performance groups (herding, hunting and guarding). The experiment consisted of the intelligence quotient test (IQ). On account of the fact that particular dogs within particular breeds achieved both the worst and best scores, it was affirmed that Coren's test could not be a determinant of the intelligence level for a given breed. Developing a ranking of the most and least intelligent breeds only on the basis of Coren's IQ test was overly simplistic, which seemed to discriminate against some dog breeds. An attempt to create an IQ ranking for dogs required taking into account such factors as the position in a pack (family) and performance. The intelligence of diverse breeds such as hunting or herding dogs should not be compared, because they had consolidated different mental features during their domestication and breeding selection. Therefore, making such a ranking might be more reasonable within particular performance groups.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".