Bibliographic record
Abstract
Describes the application of a 2D color image display to monitor various paper properties of a paper machine reel. These properties include caliper, opacity, moisture and basis weight. The conventional method of determining the paper quality of an entire reel was to perform statistical calculations on a profile-to-profile basis of paper properties in both machine direction, length and cross-machine direction, and width of the sheet. On a profile-by-profile basis, it can be somewhat difficult for mill personnel to determine whether the entire reel is within the desirable grade specifications or not, or whether the specification is only out of limits at certain sets or rolls within the reel. This difficulty can be overcome by use of digital imaging technology. An image display can be used to monitor the entire reel sheet for various paper properties. This means less time will be required for statistical calculations. Feedback from analyzing the reel image would enable mill personnel to identify possible problems associated with the paper machine controls. In addition, trimming information can be derived from the reel image noting if any undesirable streaks are present in the sheet. The reel image display was developed as a graphical tool for the paper machine, as part of a mill-wide information and optimization system (MOPS). This paper presents a case study of using this display at the Alberta Newsprint Company (ANC) pulp and paper mill. The overall benefits of this display are demonstrated by using actual property profile data recorded from their paper machine.>
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.007 |
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".