The GEOIDE Network –Integrating Research and Development for the Benefit of the Canadian Geomatics Industry
Bibliographic record
Abstract
There is growing recognition that the set of technologies and tools embraced by the label of ``geomatics'' (and other, related labels, such as ``geo-informatics'' and ``geographical information science'') has a very large potential for growth.Recent estimates put the possible ceiling on the market as high as 15% of the Gross Domestic Product.This is not hard to believe when we consider the role played by spatial information in many of our key industries.For transportation alone, which represents roughly 10% of the GDP, spatial information and its related products may represent a quarter of the market place.Likewise for commerce, which represents one fifth of the GDP, geomatics can be expected to play a major role in the future.Health needs for geospatial data are on the rise, as is the case for many other areas of public interest. Canada's Role in Geomatics DevelopmentGiven these conditions, it is clear that across the world, geomatics will play an increasingly important role in the twenty-first century.Due to Canada's long tradition in research and development within the field of geomatics and its industrial base in this area, the Canadian geomatics community believes it is well-positioned to play a major role in this development.However, Canada also must overcome a certain number of difficulties in order to take full advantage of this potential.Canada's small population provides at best a limited market for niche geomatics technologies and information products.In addition, the sheer size of the country has inhibited the development of a cohesive national policy in geomatics that responds to the needs of all the relevant communities.Efforts by the federal government have succeeded partially, but cannot be expected to mobilize the entire R&D community on their own.Within any particular region, the critical mass needed to ensure adequate movement of R&D results from the universities to industry cannot be easily managed.
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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 | 0.003 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".