Bibliographic record
Abstract
Taking the 4 619 and 403 literatures regarding research on forest carbon sink from Web of Science database and China National Knowledge Infrastructure(CNKI) database respectively as the objects,the paper analyzes and processes relevant cited data and topic key words with CiteSpace Ⅱ,and combs significant academic institutions both home and abroad,academic literatures and figures as well as research focuses concerning study on forest carbon sink by means of knowledge mapping.The results show that:(1) America,Canada,China and Germany etc.are principle nations researching forest carbon sink in the international arena,and America is holding the dominating position;(2) Major scholars conduct research on carbon cycling of forest ecosystem,the mechanism of carbon sequestration in soils,the ability of carbon sequestration for various sorts of forests and trees etc.from the angle of environmental science,ecology and forestry,and there are some scholars who perform conducive exploration from the angles of forest management and carbon sink trading;(3) Research focuses emerge in endlessly,and research area becomes increasingly mature with each passing day;(4)Research on forest carbon sink enjoys a late start domestically,and the number of issued documents demonstrates a trend of increasing.Institutions centering on Chinese Academy of Sciences and Peking University etc.take an internationally leading position in studying forest carbon sink,and the research focus is acting on the international convention.China’s international status in research field of forest carbon sink needs to be improved;(5)Low-carbon economy and carbon sink market etc.mark the research focus of forest carbon sink study in domestic management discipline.
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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.055 | 0.055 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".