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 explosion of "big data" applications imposes severe challenges of speed and scalability on traditional computer systems. As the performance of traditional Von Neumann machines is greatly hindered by the increasing performance gap between CPU and memory ("known as the memory wall"), neuromorphic computing systems have gained considerable attention. The biology-plausible computing paradigm carries out computing by emulating the charging/discharging process of neuron and synapse potential. The unique spike domain information encoding enables asynchronous event driven computation and communication, and hence has the potential for very high energy efficiency. This survey reviews computing models and hardware platforms of existing neuromorphic computing systems. Neuron and synapse models are first introduced, followed by the discussion on how they will affect hardware design. Case studies of several representative hardware platforms, including their architecture and software ecosystems, are further presented. Lastly we present several future research directions.
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) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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