Bibliographic record
Abstract
The rising tide of data from Sensor Networks, Internet of Things devices and novel sensing systems (e.g. smart devices and wearable technology) are introducing a number of opportunities as well as challenges. While the diversity and abundance of sensing resources are providing a wealth of data for Information Services, we are facing growing challenges in coping with the volume, diversity, and inconsistency of data, both in reported values and measures of accuracy, in addition to challenges in interoperability among these systems to truly realize ubiquitous services that can harness information from aggregated data. At a time when real-time access to data is critical to many applications, especially decision-making processes, the status quo in coping with Big Sensed Data (BSD) is faltering. In this paper we build on recent advancements in interoperability, and frameworks for managing IoT, to present a framework for heterogeneous sensing in BSD (HetSense-BSD), which adopts a multi-phase approach in soliciting heterogeneous resources to serve Information services. We introduce a novel Selective Sensor Fusion (S2F) algorithm for pruning superfluous data at the source, to the reduce communication footprint of data with inferior quality, and better utilize access networks for delivering the best possible data from available resources to the services. We present a use case for HetSense-BSD, and elaborate on the design of this framework as a first milestone in harnessing the aggregated potential of ubiquitously available resources in novel sensing systems.
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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 | 0.010 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".