A bibliometrical analysis of competitive situation in international ecological research
Bibliographic record
Abstract
Nowadays,ecological and environmental problems have attracted much attention of governments and people,because ecological research can provide the theoretical basis and guideline for the coexistence between humans and the natural ecosystem.In this paper,analytical tools such as Thomson Data Analyzer,NetDraw and Aureka in combined with pathfinder algorithm were used to analyze the data of ecological research in the SCIE and SSCI databases.We find that the papers of the Northern America,Europe,Australia and their institutions have stronger impact on international ecological research and their quality is better.Meanwhile,the United States is the international center of the cooperative research web in ecology,followed by the United Kingdom and Germany.At institutional level,University of California Davis and Max Planck Institute are two distinctive centers for cooperative research in ecology.The numbers of papers on ecological research in China is ranked the eleventh,but the quality of the papers is still low.The main countries in collaboration with China include the United States,the United Kingdom,Canada,Germany,Japan,Australia and France in ecological research.In the years from from 2008 to 2010,the hot spots of international ecology are mainly focused on biodiversity,climate change,gene variation,interactions among species and sexual selection,etc..
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.043 | 0.000 |
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 teacher head, 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".