Bibliographic record
Abstract
This study compares the strength properties of Jumchi-Hanjis dyed with 70% and 100% persimmon juice concentrations and undyed Jumchi-Hanjis. The Juumchi-Hanjis were made from Dakjis (mulberry papers), which were mixed with different ratios of fibers from paper mulberries originating in Korea and Thailand, including wood pulp from Canada. Research results showed that tensile, wet tensile, and bursting strengths of Jumchi-Hanjis dyed with 70% concentration were higher than those of undyed Jumchi-Hanjis. However, the tearing strengths of the dyed Jumchi-Hanjis were lower than those of undyed Jumchi-Hanjis. The wet tensile strengths of Jumchi-Hanjis dyed with 100% concentration were higher than those of dyed with 70% concentration. The increase and decrease of tensile, tearing, and bursting strengths depending on persimmon juice dyeing differed as per the mixing ratio of the raw materials of Jumchi-Hanjis. Dyeing with 100% persimmon juice concentration tends to be more useful than 70% to increase the tensile (MD) and wet tensile strengths of Jumchi-Hanjis containing only Korean mulberry fibers (90%) and wood pulp (10%) as raw materials. Dyeing with 100% concentration tends to be less useful than 70% to increase the tensile, tearing and bursting strengths of Jumchi-Hanjis with high proportions (90% or 60%) of mulberry fibers from Thailand.
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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".