Bibliographic record
Abstract
Woodcraft activities have an inseparable relationship with our daily life, and it is a field that needs to be continued because of the value of education for the growing students. The interest in woodworking from childhood to old age is rapidly expanding nowadays, therefore this study has been done to provide images to those who are engaged in woodcraft business and also those who are interested in this field. If we look at the use of wood in our daily life, We can classify it into Architecture, Civil engineering, Furniture, Musical Instrument, Packaging, Recreational instrument, Exercise instrument, Stationery, Daily commodity, and Industrial use. Among them, We examined kinds of stationery and which type of woods were used. As a result of classifying 101 stationery products in 22 countries, stationery materials using wood can be used for Business cards, Envelope houses, Box houses, Pen holders, Locker plates, Stationery baskets, Book holders, Stamps, Paper knives, Bookmarks, and Photo frames. It was found various wooden stationery are made in USA, Japan, UK, Canada etc. And the most frequently used species are hardwoods such as Walnut (Juglans regia), Maple (Acer spp.), Cherry (Prunus serotina), Birch (Betula spp.), Mahogany (Swietenia macrophylla), Tulip (Liriodendron tulipifera Linnaeus), Bubinga (Guibourtia tessmannii J. Leonard), Wenge (Milletia laurentii De. wild), Cocobolo (Dallbergia cultrata Grah), Zebrawood (Microberlinia brazzavillensis A. Chev.) and Ebony (Diospyros spp.).
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.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.207 | 0.076 |
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".