Petpaws: A Comprehensive Dataset and Recommender System for Canine and Feline Breeds
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This research proposes a dataset and a recommender system for recommending canine and feline breeds, comprising one of the largest collections of its kind with 369 canine breeds and 69 feline breeds. The dataset is distinguished by its meticulous selection of breed-specific attributes, such as adaptability, trainability, weather conditions, economy, location, grooming demands, exercise needs, friendliness towards strangers, and other relevant factors. The proposed recommender system for canine and feline breeds utilizes a rank-based, content-based, and collaborative filtering approach for recommendation that incorporates user preferences to recommend breeds that best match their requirements. The system is trained using cosine similarity to optimize recommendation accuracy and enhance user satisfaction. This research represents a significant contribution to the field of pet recommendation systems and offers valuable insights into the selection and matching of canine and feline breeds to specific user needs.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it