Exploring The Use Of AI Technology To Help Owners Remotely Accompany And Care For Their Cats
Bibliographic record
Abstract
There are many domesticated cats in Canada. Caregivers need to provide domesticated cats with a safe space and daily games that simulate hunting because of cats’ hunting nature, and they should also be mindful of the amount of exercise and food they eat to prevent obesity. However, many carers are too busy with their lives, resulting in them not being able to provide an ideal life for their cats. This thesis project will use the Research through Design (RtD) approach to explore how to employ COCO object detection model, Arduino, and Internet of Things (IoT) to design for the domestic cat's needs when people aren't at home. The research project iterated on four prototypes: 1) a safe space for cats - the Cat Castle. 2) a smart cat teaser to mimic the hunting game, which uses COCO object detection and Arduino. 3) An auto feeder to encourage cats to exercise more. 4) Integration of the above three prototypes to form an early-stage smart and cat-friendly environment. Finally, the prototype is designed to meet some of the cat's needs and it can also accompany the cat when the carer is not at home. This study can provide some exploratory experience in the animal-computer interaction (ACI) field of research on related topics.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".