Bibliographic record
Abstract
“When sorrows come, they come not single spies but in battalions.” William Shakespeare Toward a Scalable Abstract Calculus The canonical contexts sketched in Section 4.3 and employed throughout Part II were intentionally low-complexity problems. Such problems provided venues for fleshing out complete software solutions from their high-level architectural design through their implementation in source code. As demonstrated by the analyses in Chapter 3, however, the issues addressed by OOA, OOD, and OOP grow more important as a software package's complexity grows. Complexity growth inevitably arises when multiple subdisciplines converge into multiphysics models. The attendant increase in the scientific complexity inevitably taxes the hardware resources of any platform employed. Thus, leading-edge research in multiphysics applications must ultimately address how best to exploit the available computing platform. Recent trends in processor architecture make it clear that fully exploiting the available hardware on even the most modest of computing platforms necessitates mastering parallelism. Even laptop computers now contain multicore processors, and the highest-end machines contain hundreds of thousands of cores. The process of getting a code to run efficiently on parallel computers is referred to as getting a code to scale , and code designs that facilitate scaling are termed scalable . The fundamental performance question posed by this chapter is whether one can construct a scalable abstract calculus . The Sundance project (Long 2004) has already answered this question in the affirmative for C++.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.037 | 0.012 |
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".