Sistem Pakar Diagnosa Penyakit Flutd (Gangguan Saluran Kemih) Pada Kucing Menggunakan Metode Case Based Reasoning
Bibliographic record
Abstract
Cats are one of the beloved animals that require care and development. As beloved animals, cats have their own charm thanks to their different body shapes, eyes and fur colors. Cats whose pedigrees are officially registered as purebred cats. Cats, in Latin Felis silvestris catus, are a type of carnivore. The word cat usually means a domesticated cat but can also refer to large cats such as lions, tigers and leopards. Cats have been mixed with human life for at least 6000 years BC, since cat skeletons were discovered on the island of Cyprus (Arquitectura et al., 2015). One of the diseases that often appears in cats is feline lower urinary tract disease (FLUTD), also known as feline urological syndrome (FUS), which is a health problem that often occurs in cats, especially male cats. This health problem attacks the cat's bladder and urethra. Urethral disorders are caused by the structure of the male cat's urethra, which is tube-shaped and has a narrow section, which often causes obstruction of urine from the bladder (VU) to the outside of the body. FLUTD includes several diseases that occur in the cat's urinary tract (Indonesia, 2022). Knowledge about diseases in cats is one of the problems. Many owners do not realize that their cats are suffering from diseases that can cause death. The cause of the cat's death occurred due to the keeper's lack of knowledge regarding the disease and symptoms the cat was experiencing. Apart from that, the problem is that there are so few doctors available that they are difficult to find, and the information obtained is only in accordance with the condition of the cat when it goes to the vet. If you see other symptoms, like it or not, you have to consult the veterinarian again and it will take additional time and money. This problem can be solved with an expert system that can diagnose the disease suffered by the cat based on the selected symptoms.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.004 |
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".