CGDI IN ACTION: EXPLORING QUALITY OF SERVICE
Bibliographic record
Abstract
The national GeoConnections program has funded many projects for sharing geospatial information in its priority policy areas – public health, public safety and security, environment and sustainable development, and matters of importance to aboriginal communities – resulting in many geospatial Web Services in the Canadian Geospatial Data Infrastructure (CGDI). To build user confidence in using geospatial Web Services in the CGDI for their decision making, knowing the quality of these services is important. In this paper, the authors discuss the results of a GeoConnections-funded project in exploring the Quality of Service (QoS) metrics and applying them to test services in the CGDI. Essential QoS metrics, including availability, reliability, time latency, response speed, performance testing, and load testing, were determined. All these QoS metrics can be dynamically monitored by machines through simulating service requests at certain time intervals. The selection of candidate services for testing were from GeoConnections projects and from services discovered in the GeoConnections Discovery Portal, with the balanced representation from different levels of government and the private sector, communities of interest, and geographic regions. Based on the designed QoS metrics, tests were carried out for these candidate services. The availability and reliability were evaluated using the Federal Geographic Data Committee (FGDC) Service Status Checker. Other QoS metrics – time latency, response speed, performance testing, and load testing – were measured using Proxy Sniffer™. In conclusion, this study proposed and implemented QoS
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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 | 0.013 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".