Mechanical and physical properties of particleboard made from two pulp and paper mill secondary sludges
Bibliographic record
Abstract
To investigate environmentally friendly alternatives for sludge disposal, three proportions of secondary sludge (SS) from two pulping processes (Kraft and TMP) were incorporated in the formulation of particleboard manufacturing. A 3 2 factorial design was used where the factors were Urea-formaldehyde (UF) content (5%, 7%, and 9% dry weight of resin per dry weight of particles) and secondary sludge percentage (75%, 100%, and 125% dry weight of SS per dry weight of resin). For each pulping process, 27 panels with SS and 3 control panels (without SS for each resin content) were made for a total of 63 panels. All panels were tested for thickness swell, linear expansion, internal bond strength (IB), flexural modulus of elasticity (MOE) and flexural modulus of rupture (MOR). Results indicated that particleboards made with SS from both pulping processes met the ANSI standards for linear expansion, IB, MOE and MOR. However, none of the tested panels met the standard for thickness swell and adding SS to the formulation affected negatively this property. It was concluded that SS from TMP and Kraft mills can be used to manufacture particleboard panels. However, its' percentage along with other additives' content should be optimized.
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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 | high |
| 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 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 | 0.001 |
| 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, 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".