Bibliographic record
Abstract
In an ideal world, communications cabling for process control would be simple-buy all the computer, instrumentation, and electrical equipment from a single vendor, and connect it all together using a single cabling standard. But real life is never that simple; rarely are the programmable logic controllers (PLC), distributed control systems (DCS), drives, motor controls, field instrumentation, and computers all purchased from the same vendor. Supplying power to all this different equipment certainly doesn't require separate cabling structures, so why shouldn't the same be true for communications needs? Wouldn't a standard cabling infrastructure minimize the cabling infrastructure cost and complexity? The engineering group of a Canadian pulp and paper mill wondered about these two questions. They were designing a new steam plant and decided to investigate the possibility of making a single process communication cabling "utility" through the plant. The result was a design methodology that allowed a standardized cabling system to serve all communications needs throughout the process areas. Fiber-optic cable was chosen for all communications cabling outside of the control or electrical rooms. While the noise immunity and high data carrying capacity of fiber-optic cable was a factor, the primary reason was that fiber-optic cabling was the only system that could provide a single medium suitable for the very wide range of communications equipment in the mill.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".