Bibliographic record
Abstract
Information and data gathering are closely linked. Many systems are based on gathering sensor data from multiple nodes and processing centrally (cloud based). Since accessibility to increasing amounts of data leads to better decisions, this puts an increasing processing pressure on the cloud. Coupled with better and more capable sensors which are now available, data gathering has grown and accelerated into almost all applications. While it is a clear expectation that this increased amount of data will improve outcomes, it is also clear that the increasing rate of sensor data must be processed at the same rate. The computation of local data remotely creates a bottleneck to the cloud resulting in long latency. Decisions may arrive back too late to determine the best course of action. Furthermore, remote servers must share their resources according to a strategy that may not be beneficial to the critical task being controlled. Untimely computation breakdown may make critical computations difficult or completely unavailable if out-of-range highlighting the need to provide computing intelligence and decision making on the edge. Recently, edge processing has been proposed and may be the only reasonable answer. With sufficient computing capability it can provide decisions with local data quickly bypassing the latency of a cloud connection. Even in the larger context where cloud computing is required, local computation preprocesses the data resulting in better utilization of the edge-cloud transmission link. As an illustration of this type of capability, an asset tracking demonstration with real hardware was generated at ON Semiconductor. This tracking system utilizes Bluetooth tag transmitters on each asset and multiple receiving antennas connected in a network detecting multiple angle-of-arrival (AoA) from each tag. The demonstrator system determines the tag location from these measurements.
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 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) | 0.017 | 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 teacher head, 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".