Separation Anxiety in Canines and Potential Mitigation Options
Bibliographic record
Abstract
Separation anxiety is a very common disorder that many domestic dogs suffer from. It comes second only to aggression in terms of behavioural disorders reported and is the most common anxiety disorder in dogs. Canine separation anxiety has a vast range of symptoms, including excessive vocalisation, destruction, inappropriate elimination, hypersalivation, and restlessness. Since these symptoms only occur in the absence of the owner and vary between dogs, separation anxiety is exceedingly difficult to diagnose. This disorder is potentially affected by numerous factors, including gender, breed, existing behavioural issues, the degree of attachment to the owner, and environmental factors, such as the source of the dog. Fortunately, options for anxiety mitigation exist, including proper training of the dog, as well as medications such as fluoxetine and clomipramine. It is imperative that a proper diagnosis and treatment is given to the dog early in the condition, as this disorder can negatively affect both the mental and physical health of the dog, as well as the owner-dog relationship. Unfortunately, the daily struggles of managing dogs with such a disorder take a toll on the owners. This potentially results in the all too common decision to surrender the dog to a shelter, and potentially euthanasia. Ultimately, this disorder still requires future research in many aspects, in order to better the lives of both the dog and its owners.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".