Research on Intelligent Management System of Meteorological Archives Based on Big Data Framework
Bibliographic record
Abstract
The era of big data, analysis, and artificial intelligence is a new trend in intelligent big data analysis. The present stage of the geoscience progress allows the Earth to be analyzed as an extremely dynamic structure of multiple elements, such as the hydrosphere, lithosphere, and atmosphere, interacting in and with others. To derive useful information from them, large quantities of observation and simulation data provided by numeric models need to be analyzed. Visualization is a critical feature of data analytics since it is a simple and swift way to evaluate the data and consider the specific aspects and mistakes of the dataset. A geographic information system (GIS), the most efficient meteorological data visualization software class, provides excellent capabilities for geospatial data manipulation. The processing architecture that can efficiently be used as a back-end for GIS by providing quick access to the data stored at remote storage nodes is described in this paper. Weather departments use various kinds of sensors for data collection such as temperature, humidity, etc. The number and speed of the sensors in each sensor complicate the data processing time. This paper seeks to provide a weather-temperature analysis big data forecast architecture based on the MapReduce algorithm. The suggested intelligent management system of meteorological archives based on big data (IMS-MABD) framework methodology could promote research and advancement of intelligent big data analysis, large data analytics, business intelligence, artificial intelligence, and data science. Intelligent management systems for meteorological archive systems based on large data frameworks could be used. Experimental findings show that the architecture created allows real-time data access and can support many simultaneous applications successfully with a performance of 98.1%.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".