The Canadian Geospatial Data Infrastructure, achieving the vision of the CGDI
Bibliographic record
Abstract
Developments in information technology over the past decade have exponentially expanded both the need for geospatial information and the access to this information. To meet this demand, the Government of Canada invested $60 million in the GeoConnections initiative with a goal, among others, of accelerating the development of the Canadian Geospatial Data Infrastructure (CGDI). GeoConnections will come to a close in March 2005, but a plan and roadmap are needed to continue the important work of building the CGDI. This roadmap provides the direction to achieve the CGDImp;gt;'s vision. The future for the CGDI looks bright, but action, effort, and perseverance will be necessary to make the CGDImp;gt;'s vision a reality. The roadmap complements the vision and architecture work by providing key goals and tasks to tackle in the short, mid and long term. The CGDI has matured technologically to the point that we can now explore the issues and opportunities that will shape its final form. Great strides have been made, but refinement and greater participation are needed to ensure a critical mass of geospatial data, services, applications and users. The CGDImp;gt;'s external and internal environments need to be explored to understand the opportunities and challenges that lie ahead. The CGDImp;gt;'s success has been founded on strong partnerships with external and internal stakeholder communities. These types of partnerships should continue to play an important part in the future of the CGDI. The CGDI must look to associations, academia, standards organizations, industry and all levels of government for continued guidance, support and contributions. The vision for the CGDI is to enable access to the authoritative and comprehensive sources of Canadian geospatial information to support decision making. The mission statement furthers the vision by outlining how a future coordinating organization will need to operate to achieve the vision of the CGDI. The CGDImp;gt;'s mission comprises specific objectives. Each objective has corresponding goals that indicate how the CGDI should evolve . These goals also indicate what results need to be achieved. The mission, however, does not explain how to produce these resultsthat role falls to this roadmap and its goals and tasks. These goals and tasks can be grouped into four streams: coordination, users, content, and infrastructure. This roadmap identifies goals and tasks for each of these areas to reach the short-, mid-, and long-term milestones established in this document. In the short term, funding sources and governance issues will remain in the forefront. In the long term, it will be essential to engage new communities of practice and to have most provinces, territories, and several large municipalities contributing data to the CGDI and using the CGDI. Leadership and technology have been the focus and strength of the CGDI to this point. The next era of the CGDI needs to focus on service delivery and nurturing strong user communities that have the capacity to work with the CGDI.
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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 | 0.006 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".