Silvikültür ve İklim Değişikliğinin Bibliyometrik Analizi: Eğilimler, Örüntüler ve Araştırma Sıcak Noktaları
Bibliographic record
Abstract
This research presents a bibliometric analysis of scientific studies addressing the intersection between silviculture and climate change. Analysing 478 studies published between 1993 and 2023, this analysis reveals research trends, international collaboration networks, and the geographical distribution of scientific production on the topic. In recent years, especially after 2010, interest in the role of silviculture in adapting to and mitigating climate change has grown rapidly. The analysis of collaborative networks highlights the central role of the United States in this field, with countries such as Germany, Canada, and Spain also making important contributions through cross-border research partnerships. The United States and Europe are at the forefront of scientific production, revealing a growing awareness of the relationship between forest management practices and climate change. The research shows that key concepts such as 'forest management', 'carbon sequestration', and 'resilience' are becoming increasingly prominent, and research is focusing on sustainability and climate change adaptation strategies. In conclusion, this study highlights the importance of increased global collaboration and multidisciplinary approaches in research on climate change and silviculture, and provides trends that will contribute to the development of sustainable forest management policies.
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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.027 | 0.026 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".