Applications of Remote Sensing as Climate Resilience Technique: A Bibliometric Research Trends Analysis
Bibliographic record
Abstract
Regional variances in climate, soil, and topography make agricultural production systems particularly fragile. Animal health is negatively impacted by variations in air temperature, precipitation, frequency, and intensity of extreme weather events. For its assessment and administration, cutting-edge methods like remote sensing (RS), global positioning systems, and geographic information systems (GIS) might be very beneficial. The RS and GIS are essential tools with numerous applications for tackling these problems. The impact of climatic and human-induced changes on the environment is receiving more attention as a result of climate change (CC). Due to CC and human activity, “desertification” describes the degradation of land in arid, semiarid, and dry sub-humid regions. Natural resource sustainability in changing climates can be obtained with the application of RS and GIS. In this review article, the issues toward wildlife were demonstrated and the application of RS was discussed to reduce the impact of CC to save the wildlife and its preservation. Further, the bibliometric analysis was conducted via R-studio Bibliometric tool, which entailed that developed countries (USA, Canada, Germany) are more forward to applying RS tool to mitigate climate risks. Received: 18 June 2023 | Revised: 25 July 2023 | Accepted: 16 September 2023 Conflicts of Interest The author declares that she has no conflicts of interest to this work. Data Availability Statement Data available on request from the corresponding author upon reasonable request. Author Contribution Statement Arpita Ghosh: Conceptualization, Methodology, Software, Validation, Investigation, Resources, Data curation, Writing - original draft, Writing - review & editing, Visualization, Supervision, Project administration.
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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 | 0.012 | 0.061 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".