Web feature services, considerations for CGDI government partners
Bibliographic record
Abstract
In reviewing WFS in the context of the Atlas of Canada's desire to further integrate into the data management and dissemination processes of CGDI partner programs, there are four main areas of consideration. 1. WFS digital rights and business process support; 2. WFS security and performance; 3. WFS as a service used for data dissemination between data stakeholders at the Municipal, Provincial, and Federal levels; and 4. WFS as a potential technical aggregator of feature and attribute data for service to client applications. Each area of consideration for the use of WFS needs to be considered independently to ensure any limitation in one area does not overshadow the overall potential use associated CGDI endorsed specifications. Many organizations have initiated implementation of WFS and have ended up serving GML files instead. As a result there are few true WFS implementations active for comparison or review. Those implementing WFS indicate performance issues, GML version changes and limited client application adoption of WFS capabilities as reasons why WFS has seen a slower adoption than WMS. WFS services which have been largely successful, such as the Geographic Names of Canada WFS, are focused on point geometry types with small numbers of returned records and attributes. In addition WFS and WMS used within the same application based on the appropriate user functions and relative data scale may provide the content rich data of WFS within the performance expectations of users. Overall WFS has capabilities that are best applied to data transfer and processing applications where access times to data are expected to be slower but the resulting analysis product is of great value. In considering the use of WFS within the broader CGDI it is best to relate its use back to specific functional processes where exchange of vector data and attributes results in maintenance of distributed data nodes. WFS's are not a high priority to average end users. WFS/GML remains a capability utilized by GIS professionals and students with a technical knowledge of the use of GIS. Recent developments in simplifying the GML schema, GMLsf, will allow vendors and users to operate on a static GML schema and in turn begin to overcome some of the performance limitations of WFS 1.1.0 GML 3. As this new GML standard takes hold CGDI government partners will be able to move forward and take advantage of WFS in three main areas: 1. Providing data for aggregation into National Frameworks, for example GeoBase, as part of an automated mechanism; 2. Managing data by acquiring it from source asynchronously to the request/response processes for current client applications; 3. Develop linked attribute databases with separately stored geographic features linked at runtime or as part of an update process; 4. Investigate end client uses of WFS and incorporate functionality back into CGDI client applications, therefore addressing users' needs. WFS represents a standards based approach to dealing with vector data and attributes therefore opening up the data management capabilities of the Atlas of Canada to integrate and participate with partner organizations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".