Computational Vector-Magnitude-Based Range Determination for Scientific Abstract Data Types
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
As interest mounts in using hardware accelerators to speed up numerical scientific calculations, automation tool support is required to aid designers in mapping applications to custom hardware. One key step in designing this custom hardware is bit-width allocation where the known-art faces challenges when dealing with applications from the scientific computing domain, thus motivating the use of computational methods based on Satisfiability-Modulo Theory. Many real-life applications are, however, specified in terms of vectors and matrices which are of sufficient size to make expansion into scalar equations infeasible. The proposed vector-magnitude method and its extension via block vectors enable computational methods to be leveraged in tackling calculations of practically relevant complexity. Application to case studies confirms that through a more compact computational instance, search efficiency is improved leading to tighter bounds and thus smaller bit-widths.
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 | 0.001 |
| 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