Journal of Forest Business Research: an open forum to support sustainable forest investment and management
Bibliographic record
Abstract
The Journal of Forest Business Research (JFBR), an international peer-reviewed and open-access journal, provides a novel publication channel for scientific research in sustainable forest investment and management. The journal strives to meet the growing demand of scholars and practitioners in understanding sustainable forest investment and management by bringing together scientific and professional research in this field. The following section describes why there is a need for the JFBR and what makes this journal an open forum to support sustainable forest investment and management. Then, we summarize all the papers included in our two issues in 2024. This year, we published over 270 pages of high-quality forest business research. These articles discussed, among others, trends of forest investments in Latin America, Colombia’s market for manufactured wood products, wood flow management software, modeling of the growth of teak plantations, commercial tree farming in Colombia, plantation forestry in Paraguay, Brazilian timber market, export prices of Canadian forest products, Texas trade in forest products industry, craft industry in British Columbia’s forest sector of Canada, and wood prices in the Southeastern United States. All these articles demonstrate the international characteristics of forest business research. In the final section, we elaborate on what types of articles we are seeking and how you can support JFBR.
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 | 0.025 | 0.083 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.010 | 0.012 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.034 | 0.026 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.009 | 0.008 |
| Insufficient payload (model declined to judge) | 0.082 | 0.038 |
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".