Cellular biopolymers and molecular structure of a secondary pulp and paper mill sludge verified by spectroscopy and chemical extraction techniques
Bibliographic record
Abstract
For proper treatment, recycling, or disposal of the pulp and paper mill secondary sludge qualitative and quantitative determination of its characteristics are necessary. Chemical extraction, quantitative characterization, and spectroscopic experiments have been performed to determine the molecular composition and chemical functionality of a pulp and paper mill secondary sludge. In order to extract the low-molecular-weight substances, soxhlet extraction with polar and non-polar solvents was performed where most of the target substances (17±1.3%.) were extracted after 2 hours. Over time, this extraction followed a first-order kinetics. Fiber analyses have shown 12±3% lignin, 28±3% cellulose, and 12±4% hemicelluloses content. The ash content was about 17±0.5%. In this work, 7 and 16% intra- and extracellular polymeric substances, respectively, were extracted from the secondary sludge. EPS and mixture of intra- and extracellular biopolymers have shown similar chemical functionalities. These analyses confirmed that the paper secondary sludge consisted mainly of wood fiber, i.e. lignocellulosic substances, along with proteins and polysaccharides originated from microorganisms.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Bench or experimental | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Bench or experimental | high |
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 | 0.000 | 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) | 0.001 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".