Trends in research on wood forest products: a bibliometric analysis
Bibliographic record
Abstract
Considering that the global trade in forest timber products is prominent, academia needs to obtain directions for future research on forest timber products. This study aims to identify the trends and growth of publications on timber forest products, identifying driving, emerging, or crosscutting themes. The database used is Scopus, the search terms were "wood forest products" and "timber products" and the period was from 1955 to 2022. The bibliometric method was applied, using the Bibliometrix package and the interface Biblioshiny, from the R software. The results show that 89.06% of the 1,007 studies returned come from the USA, Canada, and China and that most of the publications are concentrated in three international journals. Themes that have recently emerged, such as climate change mitigation, bioeconomy, climate change, carbon stocks, plywood, biomass, and international trade, indicate a trend and suggest possible gaps to explore in future research. The contributions of this study are in the identification of driving and emerging/transversal themes that can direct future research on wood forest products, as well as already consolidated themes that no longer represent research trends. The limitations, on the other hand, are related to the collection of data only in the Scopus database and the quantitative analysis of topics based only on keywords contained in parts of the documents. Future studies may go beyond trend analysis, performing co-authorship analyses, examining authors and their affiliations, and studying the social structure and collaboration networks. They may undertake citation analysis, using citation counts as a measure of similarity between documents, authors, and journals.
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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 | 0.009 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.187 | 0.247 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".