Implementation of an IoT based Pet Care System
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
As pet ownership is soaring each year, the demands for a higher quality of pet care products are increasing as well. This has driven the development of the Internet of Things (IoT) technology in this field. Using the technology of IoT, pet owners can remotely track their pet's activity and location, monitor their pet's health condition or even interact with their pets. All these smart pet care products are playing an indispensable role in the pet owner's daily life. In the present project, we apply the IoT technology to implement an integrated system including pet food feeder, water dispenser, and litter box, which are the three most fundamental elements that pet owners will be concerned about when they are busy or away from their pets. The three subsystems are connected to the local network with Arduino Uno boards and Wi-Fi modules. Furthermore, the data collected from each sensor are processed and displayed on a smartphone application. Thus, pet owners through only one single interface, they can obtain all the information regarding pet's food consumption, water consumption, as well as defecation timing, duration, and frequency. Additionally, a controlling function is also enabled in the application for the pet owners to dispense food anytime and anywhere. An overall statistical chart with the mentioned values is presented in the application, updating from time to time. With this pet care system in a smartphone application, we provide pet owners an efficient, convenient and low-cost tool for pet care.
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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