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
ARM-based servers are garnering increasing interest in big data processing for their low power consumption. However, they are ill-suited for compute-intensive tasks due to their poor processing capability compared to the CPUs used in a traditional server. This paper describes our early efforts to integrate the processing power of the FPGA with the ARM processor inside the Xilinx Zynq SoC. An eight-slave Zynq-based Hadoop cluster is built and a customized hardware accelerator for a standard FIR filter is implemented to demonstrate the effectiveness of hardware acceleration. The Xillybus is used for communication between the ARM processor and the FPGA fabric, achieving a bandwidth of 103MB/s. The Hadoop cluster is proved to be linearly scalable with different input sizes and numbers of slaves. Overall, the cluster achieves a 3.3-fold speedup compared to a native pure software implementation on a single ARM processor and about a 20% improvement compared to an ARM-based cluster without hardware accelerators.
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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.002 |
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