Prototype virtual database for flood management
Bibliographic record
Abstract
The purpose of this thesis is to provide a database solution for flood management. This solution has integrated Internet and database technology, as well as Geographic Information System and software programming technology. It supplies not only data and spatial data for flood management, but also model calculation and bulletin board information for users. It is called Virtual Database other than a centralized database. A Virtual Database is a distributed database system located in different existing web sites. An existing web site or a new web site will be set up as a public web site to connect all the different databases in different web sites and to supply centralized query services. Virtual Database is more advanced than a centralized database according to the real situation of distributed data sources for flood management. To test the feasibility of using such a Virtual Database for flood management, the Rural Municipality of Ritchot (R.M. of Ritchot) was selected as the case study area for use in this thesis. A web site has been set up as the public query site and various data related to flood management in this area have been collected. Most technologies for Virtual Database have been used and some programming work has been done to realize this Virtual Database for the R.M. of Ritchot. The system has been tested and the final results are satisfactory. Based on the findings from this study, it can be concluded that at the present time, it is already feasible to build a Virtual Database for flood management; however, the real Virtual Database will be much more complicated and comprehensive than the prototype Virtual Database described in this thesis.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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".