A Bibliometric Analysis of Forest Gap Research during 1980–2021
Bibliographic record
Abstract
(1) Background: Forest gaps play an important role in promoting forest regeneration and facilitating the forest growth cycle. Since the 1980s, forest gaps have been widely studied by forestry scientists. The purpose of this study was to review the global literature from 1980 to 2021, based on the scientific database Web of Science Core Collection, and to summarize the research hotspots and the trends of the forest gaps. (2) Method: A bibliometric analysis was performed using the visual analytic software CiteSpace to quantify the description of annual publications, collaboration analysis of authors, institutions and countries, co-citation analysis of cited journals, cited authors, and cited references. The keyword co-occurrence, burst, and time zone were also analyzed by the software. (3) Results: The results show that the volume of annual publications is increasing. Dr. Harald Bugmann is the author with the most published works. The most active institution is the American Forest Service. The United States, Canada, and China are the three most productive countries. “Ecology” is the most cited journal. The results indicate that the hotspot in the forest gap research has shifted, and the effects of forest environmental changes caused by forest gaps under climate change have received more attention from scientists. In the future, more attention may be paid to the role of forest gaps on near-natural forest management patterns, the effect of forest gaps on forest sustainable development, and the way to study forest gaps using lidar technology. (4) Conclusion: Our results can help to understand emerging trends in forest gap research to inform forest ecology and management.
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
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
| gpt | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Other design | high |
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 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.114 | 0.160 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".