PHYSICOCHEMICAL PROPERTIES ANALYSIS AND SIZE DISTRIBUTION RESEARCH OF MICROSTICKIES IN WHITEWATER
Bibliographic record
Abstract
Microstickies in whitewater have caused serious deterioration of paper quality and low efficiency of paper machine runnability. To solve this problem it is necessary to master the characteristics of various aspects of microstickies. In this study, the physicochemical properties and size distribution of microstickies in whitewater of three typical kinds of waste papers, old newspaper (ONP), old book paper (OBP), and mixed office wastepaper (MOW), were investigated by conventional methods and a modified Flow Cytometry Method (FCM). The results showed that white water microstickies in different kinds of waste paper have different characteristics. This is a premise for analyzing stickies problems. Furthermore, in a certain kind of waste paper, the physicochemical properties and the direct determination of size and number of microstickies particle in whitewater can be combined together and taken as a whole to account for more phenomena or deduce more mechanisms, such as agglomeration and deposition, etc.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".