Bibliographic record
Abstract
Abstract Expert systems, or more appropriately knowledge‐based systems, are essentially computer programs that encode the expertise of human problem solvers in some well‐defined domain. A practical offshoot of the field of artificial intelligence, knowledge‐based technology has emerged as an important area of computer applications, with significant business impact on the industry. This is because knowledge‐based systems can automate problem‐solving tasks that hitherto could not be computerized by conventional, numeric modeling techniques. In the domain of chemical engineering, knowledge‐based systems have been used to automate such tasks as equipment and process design, control of complex processes, troubleshooting process operations, scheduling batch processes under uncertain conditions, and simulation of discrete event operations. This article discusses the motivations and benefits of knowledge‐based technology as applied to chemical engineering problems; the differences between knowledge‐based systems and quantitative, numerical techniques; the theoretical underpinnings of the technology; approaches to selecting, designing, and implementing knowledge‐based system applications; and some of the tools available to implement such systems. Alternatives to expert systems such as neural networks (nets) and case‐based reasoning are also briefly discussed. Throughout, the concepts are illustrated using examples from chemical engineering.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.094 | 0.042 |
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".