Spatio-temporal Identification on Cross Border Collaborative Research Trend of Great Lakes by Applied Mathematics Method
Bibliographic record
Abstract
The main purpose of this paper is to evaluate the global performance and to assess the current spatio-temporal trends on research outputs of Great Lakes in North America by applied mathematics method.The computational science methods were used to survey Great Lakes related articles in the Science Citation Index (SCI) and Social Science Citation Index (SSCI) during the past decades.The Macro level, Meso level and Micro level analysis insights were employed respectively on each dataset.Great Lakes research were mainly completed by USA and Canada institutes, covering 85.87% among the global research productivities.7 academic institutes in top 10 productive institutes are located in USA and other 3 institutes are addressed in Canada.50% of the top 10 scientists belong to USA, the other 50% belong to Canada, and every expert published more than 80 papers.In addition, the cognitive learning of citations explained that Great Lakes research domain was focused by USA and Canada community in total citations with an average value on citation per paper.Sweden has the most quantity in citation per paper with a relatively high quality, while in developing countries, its quality of paper needs to be improved such as in P.R China.Institutes with high quality papers are Fisheries & Ocean Canada, University of Minnesota, NOAA and Wisconsin Department Natural Resources accordingly.Considering the future challenges of climate change and sustainability in freshwater ecosystems, it should be mentioned at this point that the essence of the water security issues should be emphasized on Great Lakes research issues.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.012 | 0.020 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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, 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".