Towards collective hyperlocal contextual awareness among heterogeneous RFID systems
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
Until recently, cases of independently operated radio frequency identification (RFID) deployments occupying a common space could be considered rare. However, the recent emergence of the RAIN Alliance and Bluetooth Low Energy (BLE) is resulting in the proliferation of fixed and mobile infrastructure for the radio-identification of both things and people through standardised passive and active RFID technologies, respectively. Consequently, today, there are everyday situations where independently operated RFID systems are likely to co-exist, both ephemerally and indefinitely. In this paper, we present a mechanism for mutual discovery and the subsequent exchange of structured data among such colocated, and often heterogeneous, systems. The resulting machine-readable real-time representation of the real-world on a human scale is what we call hyperlocal context, an open, standards-based language for the Internet of Things. We argue that hyperlocal context and the presented mechanisms foster efficient crowd-sensing which combines the complementary characteristics of both active and UHF passive RFID systems. The underlying framework has been successfully implemented in open source software with BLE supported and UHF passive RFID integration in progress. Collaboration among the scientific and industrial communities to advance standards for collective context will only become more critical as the proliferation of RFID infrastructure accelerates.
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.
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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.004 | 0.001 |
| 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