Bibliographic record
Abstract
Natural capital is an important part of the ecological service systems, a necessary condition for human survival and development, and an important support for the sustainable and healthy development of the three dimensions of society, economy and environment. Based on the core ensemble database of Web of science (WOS) database, this paper uses CiteSpace and VOSviewer software to visualise and analyze the subject development trend and hot research of natural capital from 2001 to 2022. The results show that: 1. The major publishing countries in natural capital are the United States, China, the United Kingdom, Australia and Canada, with the United States in first place followed by China, and China and the United States are far ahead of the rest of the world in natural capital research. 2. The Chinese Academy of Sciences, the Australian National University, the University of Oxford, Stanford University, the University of Queensland and Beijing Normal University are the top six publishers in natural capital research institutions, with the Chinese Academy of Sciences leading the way in terms of the number of articles published. 3. In the past 20 years, the number of articles published on natural capital research has been growing exponentially, with an explosive growth during 2013-2022. 4. Research hotspots cover resource management, sustainable development, climate change, ecological services, environmental protection and biodiversity, etc. , indicating that natural capital needs scientific and technological support from multi-disciplinary fields, and in-depth research in this field also promotes the development of other disciplines.
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 | 0.008 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.188 | 0.215 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.011 | 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".