Bibliographic record
Abstract
Laboratory testing, using the penetration instrument developed at McGill University, was conducted in order to determine the surfactants effective at improving the kraft pulping liquor penetration into aspen heartwood during the first stages of impregnation. Five surfactants, one anionic (Sodium dodecyl benzene sulfonate from Sigma-Aldrich), and four non-ionic (TRITON X-100 and X-114, TERGITOL 15-S-7, all three from Dow Chemicals, and BUSPERSE 47 from Buckman Laboratories) were tested at their critical micelle concentration. The surfactants were used either individually or as blends of one anionic and one non-ionic surfactants. Different combinations were also tested in a 4 to 1 volume ratio of white and black liquor solution. The effectiveness of the surfactants was determined according to two methods, the qualitative analysis, based on the highest liquor absorption at a specific time, and the rate method that uses the highest penetration rate at each of the four phases of the absorption. Individually, TRITON X-100, BUSPERSE 47 and SDBS were effective according to the rate analysis. Moreover, regarding the blends, except for those involving a mixture of TERGITOL 15-S-7 and black liquor, all combinations of surfactants improved the penetration of the liquor. The best performance was achieved with the blend of SDBS and black liquor.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".