국내산과 태국산 닥섬유 및 목재펄프가 혼합된 줌치한지의 감물염색에 따른 강도 특성
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it