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
In multi-hop wireless networks, random network coding represents the general design principle of transmitting random linear combinations of blocks in the same "batch" to downstream relays or receivers. It has been recognized that random network coding in multi-hop wireless networks may improve unicast throughput in scenarios when multiple paths are simultaneously utilized between the source and the destination. However, the computational complexity of random network coding, and its energy consumption implications, may potentially limit its applicability and practicality in mobile devices. In this paper, we present our real-world implementation of random network coding on the Apple iPhone and iPod Touch mobile platforms, and offer an in-depth investigation with respect to the difficulties towards such an implementation, the limitations of the ARM processor and the hardware platform, as well as our hand-tuning efforts to maximize coding performance on the iPhone platform. With our implementation deployed on both the iPhone 3G and the second-generation iPod Touch, we report its coding performance, energy consumption rates, as well as CPU usage with multimedia streaming.
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 | 0.000 | 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