Better understanding of process operation using performance metrics and visualization plots
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
In an industrial facility, a large amount of data on the operation of the facility is collected. The data includes control variables used in monitoring and regulation, alarm variables displayed to notify operators of abnormalities, and operators' actions taken to correct abnormalities as well as to operate the facility. As the data is historized and is readily accessible, it can provide a wealth of knowledge on the operation of the facility. Thus, this paper tackles the following question: Can the historized data be used to extract information to better understand process operation? More specifically, the paper attempts to answer the question by its two contributions, namely: (i) the development of performance metrics that characterize the time and frequency relations of events, and (ii) the development of two types of visualization plots to present the performance metrics in a succinct fashion. The paper also discusses the incorporation of the performance metrics and the visualization plots into industrial automation platforms.
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