DistributedGstribute Data Access on the WWW
Bibliographic record
Abstract
This thesis investigates the design and implementation of a Web-based distributed geospatial data warehouse (WDGSDW) system which allows a user to query geographical information and access the geospatial data services across multiple servers over the Internet. A multi-tiered client/server architecture was used to implement WDGSDW. The CORBA-based (for Java and C++), Java RMI-based and Java servlet-based implementations of the server-side components of DWGSDW are tested and compared for the contextual data service, which providing the user interface of WDGSDW. The comparison showed that the performance of servlets-based implementation is much better than those of other implementations. The servlets technique was chosen to implement an experimental catalog server and geospatial data servers. An integrated tool to visualize the Canada Land Inventory data (in Arc/InfoE xport.E 00 format) and raster image data was also implemented in this research. The search engine, which is the kernel of WDGSDW, supports combined text search and geographical search with an adjustable match factor. The search engine was built using R-Tree and AVL-Tree indexes. WDGSDW system was tested using test data sets containing 6979 CE ONet metadata files, 1690 CLI vector data sets and 45 CCRS raster data sets. For the contextual data server, CORBA and RMI techniques are 2 to 2.5 time slower compared to the Java servlet and a performance of 85 bytes/ms was observed for the latter, on average. The keyword searches can take up to 4.9 seconds compared to bounding box searches times of less than 2.5 seconds on a catalogue containing 8188 entries. A combined keyword and bounding box search requires an average of 1.2 times more than the individual searches. For a fixed bounding box [200, 350; 20, 84], ...
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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 | 0.005 |
| 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 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 0.013 |
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".