Occurrence of renal disorders and associated clinico-epidemiological factors in dogs of Bareilly region of Uttar Pradesh, India
Bibliographic record
Abstract
Urinary system plays an important role in the excretion of the metabolic waste from the body. Kidney is the vital organ of the body having variety of functions, important for the survivability of the animal. Any of alteration in kidney function disbalance the normal homeostasis of the body and if not noticed earlier, the alteration in kidney in chronic case may leads to the death of animals. Overall incidence of kidney failure cases from March, 2021 to August,2021 was found 1.16%. Dogs were screened for renal failure on basis of history (Toxin, nephrotoxic drug), clinical signs (vomiting, anorexia, weight loss, oral ulcer, halitosis, dullness, pale mucus membrane etc.), haemato-biochemical analysis (Haemoglobin, Packed cell volume, Total erythrocyte count, total antioxidant capacity, creatinine, blood urea nitrogen) urinalysis (proteinuria, glucosuria, ketonuria, leucocytes and Casts and specific gravity) and ultrasonography. According to different age groups it was found that highest incidence were in 4-8 yrs. of age group, followed by >8 yrs. of age group and lowest in 0-4 yrs. of age group. Highest cases of renal failure were recorded in Labrador followed by Pomeranian and German shepherd etc. In terms of males and females, males (1.24%) were more affected than Female (0.57%) out of total diseased cases of canine population from March, 2021 to August, 2021.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 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".