THE UNIVERSITY OF CALGARY A JAVA IMPLEMENTATION FOR OPEN GIS SIMPLE FEATURE SPECIFICATION
Bibliographic record
Abstract
Distributed GIS is the trend in the current GIS community. It has been recognized that interoperability is one of major issues in the distributed geocomputing environment. To respond to non-interoperability problem, OGC creates a series of specifications to form an open framework as GIS standards. These standards are increasingly accepted by GIS software vendors, geodata and geoprocessing providers, and users. This research focuses on an implementation of OpenGIS Simple Features Specification in the Java computing platform, which is an important family member of OGC’s specifications. A Java version Implementation Specification for OpenGIS Simple Features is designed in this research based on the review and analysis of the OGC Abstract Specification, OGC implementation specifications for SQL, OLE/COM and CORBA, and other related works done by other organizations. The Geometry Data Model, the spatial component of OpenGIS Simple Features, was designed and implemented following the new Implementation Specification. The Template Union Model for buffer operation was introduced, and some new algorithms were developed. The reasonable geometry object classification logic made the designed model more extendable and implementable. The UML technology and Java standards applied in the design and implementation procedures made the model more maintainable and distributable. An easy-to-use Conformance Testing Suite was also developed to check whether or not each implementation is strictly compatible with the requirements of the new Simple Features Implementation Specification. The application example demonstrated that the design and implementation of the designed specification in this thesis are successful. iii
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.034 | 0.021 |
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".