Bibliographic record
Abstract
Furniture joint one of the most critical point in furniture construction and it is one of the most governor part of the furniture construction. Therefore, many studies focuses on furniture joint from USA to China. This study aims to reveal the current structure of studies conducted in the literature on furniture joints. The study is structured in three basic stages. Firstly, a comprehensive literature study on furniture joints was carried out. Then, the sample of the study was determined as 253 articles with the keyword furniture joint in the Web of Science database. In the third stage, bibliometric analyzes were carried out. VOSviewer and Biblioshiny programs were used in the analysis, and the sample group was determined according to some parameters such as country, author, study year, university and study name. The results indicated that 2018 was the year for the highest numbers of furniture joint studies. Eckelman, Zhang, and Erdil were the pioneers authors on furniture joints studies. USA and Canada were the first countries where furniture joints studies initiated. Currently, Türkiye is the leading country on furniture joint studies, and China is getting popular country in the subject of the study. Besides, Purdue University and Mugla Sitki Kocman University is the accommodations which published the highest number studies on furniture joints. Additionally, the biometric analisys of the study showed that Forest Products Journal published the highest number furniture joint papers.
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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 | 0.032 | 0.153 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.233 | 0.271 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".