© 1996 World Batch Forum. All rights reserved. Redesigned State Logic for an Easier to Use Control Language Presented at the 1996 World Batch Forum (WBF) in Toronto Reproduced/Presented by arrangement with the copyright owner
Bibliographic record
Abstract
A previous World Batch Forum paper presented a language tailored to address process control prob-lems and radically improve Ease of Use. The present paper places that paper's Ease of Use features in explicit terms, targeting a minimal 3:1 readability and top-down understanding improvement, as mea-sured by the amount of time taken to draw simple conclusions from the visual program representation, and argues that this corresponds to a real 3:1 reduction in total cost of the application engineering. To illustrate how such a significant computing practice improvement might be driven from within pro-cess control, the paper considers the systematic redesign of just one aspect of computation, control log-ic, based on States rather than Truth values, to improve control relevance, Ease of Use, and efficiency. While other features in the language offer far greater improvement over standard practice, logic is a gen-erally understood area, and one of basic significance to Batch Control. Audience participation tests will demonstrate the intended improvement over conventional language logic. The paper then reviews other implications.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.235 | 0.058 |
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".