Clustering spatial autoregressive kriging model for climate: A bibliometric analysis approach
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Climate change is caused by temperature, rainfall, and wind variation in locations that last a long time. This change can be described and predicted using a spatial model, one of which is the Clustering Spatial Autoregressive (SAR) Kriging model. Therefore, this research aims to conduct a bibliometric analysis in a spatial and Clustering SAR Kriging model on climate change. It presents a Systematic Literature Review (SLR) with the development of the Clustering SAR Kriging model, incorporating articles from the Google Scholar, ScienceDirect, Dimensions AI, and Scopus databases from 2011-2021. Furthermore, two stages of analysis have been conducted, first, bibliometric analysis was performed for mapping and thematic evolution using VOSviewer software and R-biblioshiny. This analysis generated 185 papers after conducting a duplication check and developed a network of research on evolutionary subject matters at this stage. Second, research subjects were analyzed using the Clustering SAR Kriging model. More screening criteria were followed, and 18 articles were obtained for the SLR analysis. Furthermore, the development of the Clustering SAR Kriging model was observed for the prediction and description of climate change. The results are predicted to benefit applicable businesses to predict climate phenomena in unobserved places.
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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 | 0.007 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.014 | 0.029 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| 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 it