AUTOMATED DATA PROCESSING AND INTEGRATION OF LARGE MULTIPLE DATA SOURCES IN GEOHAZARDS MONITORING
Bibliographic record
Abstract
The development of geohazard information management system has greatly promoted the wireless automation monitoring technology for geohazards. More monitoring instruments are increasingly used in geohazard monitoring. Consequently, the types of monitoring data become more and more complicated, and massive amount of monitoring data are collected, which raises new demands in data storage and retrieval. In order to meet the requirements of data processing in geohazard monitoring, this paper presents a method of geohazard monitoring data processing, realizing the heterogeneous data integration, data access optimization, and abnormal data processing. Having analyzed the wireless automation monitoring process and the features of geohazard monitoring data, we defined the data integration standards of multiple data sources. Based on this, we developed a Geohazard Monitoring Data Integration System, with optimization in both hardware and software. This system allows automatic integration of large monitoring data from multiple sources. It has important significance for geohazard monitoring and early warning. A Geohazard Monitoring Data Analyzing System based on the monitoring data integrated by this system and data mining technology is developed to fully explore the hidden values of Big Data. Through field tests in Guizhou province with 92 sets of monitoring equipment and 5 types of databases, this method is proven to meet the system requirements with satisfactory performance.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 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".