Pet dog managemental practices followed by pet dog owners of Kakinada city in Andhra Pradesh
Bibliographic record
Abstract
An empirical study was conducted to identify the level of awareness and practices followed by pet dog owners in Kakinada city, Andhra Pradesh. The study design involved the systemic personal interview of randomly selected 127 dog owners using a well-structured interview schedule through a simple random sampling method. The study revealed that Dog owners mainly provide homemade and commercial (74.01%) food, 4.72% exclusively use commercial foods and none of them were aware of the chocolate toxicity. The majority of them (86.61%) give both vegetarian and non vegetarian foods to their pets 2-3 times a day. Pet owners in this area prefer male (59.05%) dogs over females. People in this area have a preference for the rearing Labrador Retrievers. About 85.83% pet owners were aware of heat detection, and 82.68% were aware of the appropriate period for breeding. The results revealed that, 38.58% of the owners neutered their dogs. Among the respondents who owned breeding dogs, cent percent preferred natural method of breeding by taking female dogs to male dog’s place. Majority (74.02%) of the owners took their dogs for pregnancy diagnosis and preferred both physical examination by the veterinarian and as well as ultrasound scanning (55.91%) for pregnancy diagnosis. The majority (62.99%) of the owners weaned puppies at less than 4 weeks of age. Most dog breeders (87.4%) in Kakinada housed their dogs at home without any separate housing provisions, only 12.6% kept their dogs in Kennels. The present study reveals that 84.25% of dog owners engage in deworming and vaccination with the advice of nearby Veterinarians. The majority of the dog owners (87.19%) own only one dog as a pet, and most (64.57%) of them maintain records of vaccination and disease treatment. Most of the dog owners (73.23%) do not groom hair and trim nails of their pets despite spending more than 3hrs with their pets (77.17%). Awareness of the importance of grooming and nail trimming is lacking. Approximately, 43.31% of owners in the study area take their dogs for exercise for one hour daily, both in the morning and evening. Pet dog insurance was not known to all the respondents. Therefore, there is urgent need to enhance the knowledge of the dog owners regarding scientific breeding practices of pet dogs by effective dissemination of the information based on the needs of the dog 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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".