Military canines: Contrast and comparison across countries
Bibliographic record
Abstract
Canines are utilized in the military across many countries including the United States, Australia, and Britain. However, the specific purpose, breed, and training of military canines differ slightly across these countries. The goal of this research is to conduct a systematic literature review of various databases which include information pertaining to the purpose, breed, and training of canines in the military across multiple countries including the United States, Australia, and Britain as well as analyze the history of canine use in the military branches within those countries. Research has shown that the most common breeds used today in the military, across the United States and Britain are the Belgian Malinois, German Shepherd, and Labrador Retriever, while in Australia instead of focusing on a specific breed, they select a dog based on certain traits and tendencies that are optimized for their particular role. The prominent roles military canines fulfill include bomb and drug detection, security, patrol, and tracking in each of these countries. The training of these dogs varies based on their specialty, and the duration of official training is different in each of the listed countries. Highlighting these aspects of canines in the military brings attention to the importance of their role in the line of duty as well as the comparisons and differentiations of usage across various countries. The results of the systematic literature search after excluding studies that did not meet the criteria were 27 articles appropriate to use in this literature review across the Oklahoma State University library databases.
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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 | 0.008 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.034 | 0.028 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".