CRC HANDBOOK ON ALGORITHMS AND THEORY OF COMPUTATION Mikhail Atallah, Editor Chapter 48 Parallel Computation: Models and Complexity Issues
Bibliographic record
Abstract
This chapter is an introduction to the area of parallel computation written in accordance with the guidelines for the CRC HANDBOOK ON ALGORITHMS AND THEORY OF COMPUTATION where it will appear. References to chapters refer to other chapters in this book. This research partially supported by National Science Foundation grant CCR-9209184; a Fulbright Scholarship, Senior Research Award; and a Spanish Fellowship for Scientific and Technical Investigations. Address during 1995--96: Departament Llenguatges i Sistemes Inform`atics, Universitat Polit`ecnica de Catalunya, Pau Gargallo 5, 08028 Barcelona, Spain. y This research partially supported by the Natural Sciences and Engineering Research Council of Canada grant OGP 38937. 1 Introduction Parallel computation is the branch of computational complexity theory concerned with the development and analysis of parallel computing models and the techniques for solving and classifying problems on such models. Despite technology that continuous...
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.041 | 0.030 |
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".