Assessment of wetland potential and bibliometric review: a critical analysis of the Ramsar sites of India
Bibliographic record
Abstract
Abstract Background This study focussed on effective bibliometric analysis of research on Ramsar sites in India within the last 30 years using VOS viewer software. The study focused on the estimation of the current areal extent and meteorological impact on wetland together with bibliometric analysis. The online data source of Web of Science has been used to extract all the relevant documents on Indian Ramsar sites published within a period of 1989 to 2020. Main body The main objective is to analyze the trend of research on the Indian Ramsar site both qualitatively and quantitatively. The peak of research growth has been lifted after the twentieth century as most of the Indian water bodies have been designated or put into the land from this period. A geographical location-based mapping was prepared based on the number of publications during the study period to observe the growing research interest in wetland studies in India. The highest publications have been documented on East Calcutta Wetland located in West Bengal followed by Loktak Lake in Manipur, Deepor Beel in Assam and others. The most frequently used keywords were “Ramsar site”, “India”, “wetland”, “wetlands”, “remote sensing” etc. India published most of the documents on wetland studies in the Indian context. England, Australia, Canada also had various international collaborative works with Indian researchers. Conclusions The research output shows an upward research trend on Indian Ramsar sites both quantitative and qualitatively. Such kind of research can provide a panoramic view towards the worldwide research trend and help to generate further effective approaches for the betterment of our environment.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | Bibliometrics Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Other design | high |
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 | 0.024 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.018 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".