DogMate: Dog Breeding, Grooming, Health and Vet Locator Information System for Dog Parents
Bibliographic record
Abstract
Dogs are the earliest animal to be domesticated and accompany man in his journey through history. Despite the massive differences between breeds like the Newfoundland and Pugs and Chihuahuas they are still members of the same species — Canis familiaris. However, with many breeds comes the problem of distinction and generalization and applying general knowledge on a dog can be difficult. Dog parents not understanding that caring for a dog is not as simple as just setting down a food bowl with treats or walking them for thirty (30) minutes a day often led to cases of neglect and abandonment. Knowing about the basics of a breed is the key to ensuring that the dog thrives in a household. Dog parents need information and, although much of it is accessible through the internet, not all of it can be found in just one website or one application. Using similar technology and a wider range of research, the developed website and mobile application, DogMate, showcases all three hundred forty-four (344) breeds, including general information on health and hygiene online and offline in place of needing to search of the same information through different websites online.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.115 | 0.066 |
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".