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
The adoption of the Internet of Things raises many challenges. A variety of its applications require widespread distribution and high mobility support. In addition to low latency and real time services. To meet these challenges, the Fog Computing is arguably a suitable solution to leverage the Internet of Things with such requirements. Indeed, we believe that the nearness of Fog nodes to the edge of the network provides an environment for critical preemptive and proactive applications and services (e.g., predicting natural disasters). Thus, this paper proposes an architectural model for Fog Computing. First, it presents a middleware to abstract the underlying devices and to unify the sensed data. Second, it describes an Operational Layer intended for service presentation, management and transformation. An environment embracing such model will provide means for early data analysis, hence low latency and real time responses. In addition, to providing an ecosystem for direct collaboration between services leading to more sophisticated applications. A flood warning system exemplifies a use case scenario to illustrate the potential adaption and application of the presented model.
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 | 0.001 | 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 | 0.001 | 0.000 |
| 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