Towards collective hyperlocal contextual awareness among heterogeneous RFID systems
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.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".