Intelligent information, monitoring, and control technology for industrial process applications
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abnormal event management (AEM) in large manufacturing plants has evolved as a higher and increasingly vital function of process control. In this paper, an intelligent information management and control system is introduced. The different computational agents (i.e., modules) of the system are embodied in a three-layered cognitive hierarchy, which offers intelligent behavior at the system level, as well as at the level of specialized task agents. At the lower level, agents generate goal-seeking reactive behavior. Three different fault detection and isolation agents (i.e., three complementary techniques) are embedded to generate three different assessments and to enhance the fault isolation process. Other utility agents are also incorporated to address topics such as data reconciliation, process model identification and optimization. At the middle layer, agents enable decision making, planning, and deliberative behavior. Two case-based reasoning agents are incorporated; the first manages the system in normal operation, while the other handles faulty process situations. A meta-management agent at the highest level monitors and coordinates other agents so as to make the whole system performance more robust and coherent. 1.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it