Bibliometric analysis of highly cited articles on ecosystem services
Bibliographic record
Abstract
This paper presents global research trends involving highly cited articles on ecosystem services from 1981 to 2017 based on a bibliometric analysis of such articles from the SCI-E and SSCI databases of the Web of Science. The analysis revealed that there were 132 highly cited articles, most of which were published between 2005 and 2014. Based on author keywords, the term ecosystem services was strongly linked to biodiversity. The top three journals in terms of total number of highly cited articles published were Ecological Economics, PNAS, and Ecological Indicators. Despite ranking sixth overall, Science ranked first in both impact factor and total citations per article. The US, UK, Netherlands, Spain, and Sweden were the top five most productive and cooperative countries in the world based on total number of highly cited articles and co-authorship network, respectively. The US was highly connected to Canada, the Netherlands, China and the UK. Stockholm University and Stanford University were the most productive institutions in Europe and North America, respectively. Stanford University is associated with many scholars in the field of ecosystem services research because of the InVEST model. Robert Costanza was the most prolific and highly cited author, the latter being largely due to the first valuation of the world's ecosystem services and natural capital, he and his co-authors published in 1997 in Nature. Terrestrial, urban, and forest ecosystems were the top types of ecosystems assessed. Regulating and provisioning services were the major ecosystem services studied. Quantitative and qualitative assessments were the main research focus. Most of these highly cited studies on ecosystem services are done on areas geographically located in North America and Europe.
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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 | 0.005 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.184 | 0.200 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".