Bibliographic record
Abstract
Abstract The common use of parallel computing has greatly evolved since the original encyclopedia article of 2001. Multicore processors are now quite common, so many computer users have this readily available on their desktop computers and laptops. Now increasingly large datasets and simulation of complex statistical models are important in the study of many physical systems. Models in various areas, such as environment, biology, and other physical sciences, play an important role in prediction or detection of changes. All these require lots of computing power. Many of these computations can take advantage of parallel or distributed computing. This article discusses some of these ideas and then discusses how these are implemented in one specific language, R. In the present time, one generally no longer has to work at a low‐level programming language, as was the case a decade or two ago, but now certain types of parallel computations can be implemented at a relatively higher user‐friendly level, even with desktop computing. Parallel computing consists of a computing environment connecting many processors. Instead of the previous generation where dedicated computer architecture was required, a more loose structure of distributed computing is now more common. This article is intended to give the reader an overview of the parallel computing environment, focusing on the statistical uses that can be made as opposed to a more detailed computing or engineering description.
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 | 0.008 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.011 |
| Science and technology studies | 0.001 | 0.009 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 0.005 |
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".