Characteristics and traits of selection of search and rescue dogs of different breeds
Bibliographic record
Abstract
The purpose of the research was to study and give an objective evaluation of the olfactory orientation of search and rescue dogs of different breeds with using selection and genetic parameters. In order to assess these parameters of the working traits of service dogs of different ages, data from special evaluation forms of the following breeds were used such as German Shepherd, Belgian Shepherd, Labrador Retriever, Golden Retriever, owned by the Russian Ministry of Emergency Situations. It was found during assessing the selection and genetic parameters of the olfactory orientation of dogs of different breeds that the average indicator of correctly made decisions by dogs during the study of the trail (compliance with the standard) was the highest in dogs of the lupoid type of German Shepherd breed – 82.8 %. They were inferior to males of Golden Retriever breed (P > 0.999). When certain animals interact with the environment, variability of traits occurs. Thus, in dogs of German Shepherd breed, the coefficient of variability was 5.7 %. For example, among dogs there are animals with deviations within 1σ (search performance was 77.1 %), which make a greater percentage of errors, and dogs whose performance in making correct decisions was 88.5 %. Among 22 males of Belgian Shepherd (Malinois) breed high results were also noted as 80.5 %. Dogs of this breed have animals capable of having a greater track performance of up to 84.5 %, as indicated by a coefficient of variability of 4.0 %. Within +1σ such values are allowed. It was established when used a statistical one-factor complex with fi xed indicators of olfactory orientation that among dogs of diff erent breeds the strength of the influence of origin on olfactory orientation is different.
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 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".