Bibliographic record
Abstract
An eleven-month labrador dog was brought to Veterinary clinical complex, Veterinary College and Research Institute, Namakkal with a history of anorexia, frequent vomition and progressive weight loss. Hematology and serum biochemical analysis showed severe anaemia, leukopaenia, azotemia, hyperphosphatemia and hypocalcemia. The case was tentatively diagnosed as Polycystic kidney disease based on clinical signs, haematobiochemical parameters and ultrasonography. In spite of the palliative treatment, the animal died within a week and the same was referred to Department of Veterinary Pathology for necropsy. The carcass was severely emaciated and ulcers were noticed in the oral cavity. Kidneys showed numerous, whitish, irregular and varying-sized fluid-filled cysts studded over the entire renal parenchyma. Histopathological examination of the kidney revealed large amount of fibrous tissue in the interstitium with irregularly dilated cysts and some cysts with homogeneous, acidophilic material in the lumen. On Masson’s trichrome staining and Van Gieson’s staining, the fibrous stroma appeared blue and bright red in colour respectively. The liver showed portal fibrosis with bile duct hyperplasia. This communication deals with the pathology of congenital polycystic kidney disease in a dog.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".