Use Of Transputers In A 3-d Positron Emission Tomograph
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 use of a VME-based transputer network as a parallel processing engine for positron volume imaging is dis- cussed. We find that the speedups of parallel networks depend on two major factors: the ratio of computation to communica- tion for a task, and the size of the task, and we give a simple model to explore the limits to speedups. Through actual imple- mentation we show that real-time PVI data acquisition can be achieved with about 20 transputer nodes, and we estimate that 3-D image reconstruction can be achieved within 10 min using 200 nodes. Larger images and a larger number of histograms can readily be accommodated using the same parallel algo- rithms as our model places no limits to the size of the images. The versatility and scalability of transputers makes them very suitable for use in PVI tomographs in that the same transputers can be used for speeding up data acquisition, image reconstruc- tion, and display.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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