Bibliographic record
Abstract
In the new age of technology, internet and ever-improving communications many trends and fields have been appearing, such as smart phones and their applications, internet of things, renewable energy sources and smart buildings. The latter utilizes a number of aspects from the former, as the size of the buildings keeps growing, the engineers and designers aim to reduce energy consumption and ecological footprint, make the building safer and more sustainable. This has stimulated the growth of the field of sensor networks. This paper discusses a smart sensor network which utilizes Power over Ethernet, P.o.E., supplied by a Cisco Catalyst 4507R+E switch and cloud computing to provide an easily scalable and adaptable system that would be able to adapt easily to a wide array of applications and fit the demands of new trends. The system was tested on Raspberry Pi and BeagleBone microcontroller boards as sensor hubs, and uses DigitalOcean as the cloud computing service of choice. The server in this implementation acts as a user interface, front end, and as the console unit, back end. The system has demonstrated to have fast communication times of below 200ms in a cross-continental setting and able to provide fast processing times of under 1s.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.005 |
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".