Bibliographic record
Abstract
The pulp and paper potentials of solid-waste of Ananas comosus (pine-apple leaves), Cocos nucifera (coir i.e. fiber from coconut husk), Tithionia diversifolia (sunflower) and Sansevieria liberica (mother-in-law’s tongue) were investigated. Pulp was produced by soda pulping process at liquor to solid ratio 7:1 and bleached with hydrogen peroxide in basic medium. The pulp yield of pineapple leaves, coir, sunflower, and Sansevieria liberica were 80%, 75%, 79% and 82.5% respectively. The fiber morphology were determined after maceration with 1:1 acetic acid–hydrogen peroxide ratio. The fiber length of the pine apple leaves, Tithonia diversifolia, coir and Sansevieria liberica were 0.935mm, 0.758mm, 0.894mm and 2.291mm respectively. The Runkel Ratio were 0.639, 0.540, 1.057 and 0.923mm respectively. The experimental design adopts the use of complete randomized block design on morphological examination of the solid-waste. In this configuration, we construct and solve as optimal network flow, the problem of minimizing the cost of disposing wastes from sources of generation to dumpsites by establishing processing mills in rural areas. The surface response results indicated optimum conditions for the production of good quality pulp and paper from the studied solid-wastes is when the fiber length is 0.61mm, fiber diameter is 17.16µm, lumen width is 12.40µm, cell wall thickness is 1.31µm, and Runkel Ratio is 0.2073. The average fiber length of 0.61mm coupled with low Runkel ratio 0.2073 would result in highly flexible fibers that can easily collapse to produce pulp and paper of good optical and strength properties.
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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 | 0.000 | 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, 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".