The Canadian Geospatial Data Infrastructure: Better knowledge for better decisions
Bibliographic record
Abstract
This document is an update of the original Canadian Geospatial Data Infrastructure (CGDI) Vision that was created in 2000. While consultations and research proved the original Vision to still be valid, it has been enhanced with new (*) ideas. Vision *To enable access to the authoritative and comprehensive sources of Canadian geospatial information to support decision-making. Mission Enable decision-making and policy development that address Canadamp;gt;'s priority issues such as health, social, cultural, economic, and natural resources. Facilitate access to the leading sources of Canadian geospatial information Provide continued involvement and leadership in the development of geospatial standards and specifications. Foster partnerships and sharing of geospatial information across all sectors, at all levels of government, and at the international level. Support a broad and vibrant user community. Ensure that infrastructure operations are on going and sustainable. Guiding Principles Open: The CGDI will be based on open and interoperable standards and specifications for operations and information exchange. Transparent: The CGDI will allow users to access data and services seamlessly, despite any complexities of the underlying technology. Cooperative: The CGDI will facilitate the cooperation and collaboration of participating organizations from all sectors, levels of government, and academia. Evolving: The network of organizations participating in the CGDI will continue to address new requirements and business applications for information and service delivery to their respective users. Timely: The CGDI will be based on technologies and services that support timely or real-time access to information. Self-sustaining: The CGDI will be sustained through the contributions of the participating organizations and broad user community, and through its relevance to these groups. Self-organizing: The CGDI will enable various levels of participating organizations to contribute geospatial information, metadata, services and applications. *User-driven: The CGDI will emphasize the nurturing of and service to a broad user community. Users will drive the future development of the CGDI. *Closest to Source: The CGDI will build upon its principle of self organization by encouraging organizations that are closest to source to provide data. This will increase quality and efficiency by eliminating duplication and overlap. *Secure: The CGDI will be secure and protect data that is sensitive or proprietary.
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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 | 0.028 | 0.064 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.012 | 0.023 |
| Science and technology studies | 0.014 | 0.014 |
| Scholarly communication | 0.032 | 0.031 |
| Open science | 0.008 | 0.018 |
| Research integrity | 0.006 | 0.012 |
| Insufficient payload (model declined to judge) | 0.037 | 0.013 |
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".