Bibliographic record
Abstract
“Memory is a crazy woman [who] hoards colored rags and throws away food.” Austin O'Malley The Problem Large software development efforts typically require a degree of consistency across the project to ensure that each developer follows practices consistent with the critical goals of the project. In high-performance computing (HPC), for example, Amdahl's law (Chapter 1) suggests that scaling up to the tens or hundreds of thousands of processor cores available on leadership-class machines requires that every dusty corner of the code make efficient use of the available cores. Otherwise, whichever fraction of the code speeds up more slowly with increasing numbers of cores eventually determines the overall speedup of the code. Another form of consistency proves useful when one desires some universal way to reference objects in a project. Doing so facilitates manipulating an object without knowledge of its identity. The manipulated object could equally well be an instance of any class in the project. In HPC, communicating efficiently between local memories on distributed processors represents one of the most challenging problems. One might desire to ensure consistency in communication practices across the project. In these contexts, two broad requirements drive the desire to impose a degree of uniformity across a design: one stemming from a need for consistent functionality, and the other stemming from a need for consistent referencing. Opposing these forces is the desire to avoid overconstraining the design. In the worst-case scenario, imposing too much uniformity stifles creativity and freezes counterproductive elements into the design.
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.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.012 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.095 | 0.075 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".