THE CANADIAN GEOSPATIAL DATA INFRASTRUCTURE: LOCATION-BASED INFORMATION SHARING STRATEGIES FOR THE PUBLIC SAFETY COMMUNITY
Bibliographic record
Abstract
Municipal and regional governments, provincial emergency ma nagement organizations and national public safety and security agencies are mandated with responding to public safety and security situations. There is an increasing need for inter-jurisdictional co-operation and exchange of information to deal effectively with single- and multiplemandate emergency and disaster events and to create ‘national situational awareness’. The state of public safety and security information sharing has evolved significantly over the past decade and many opportunities still exist to increase and better facilitate information sharing. Progressively, organizations have transformed their businesses to take advantage of location-based information to support complex public safety and security decisions. However, there are significant technology, policy and cultural issues constraining these opportunities. The data is often collected and maintained within various levels of governments for the business requirements of the particular jurisdiction; for various reasons, it is not shared. In many cases these data sets are only compatible with the databases and geographic information systems operating within the individual organization. In order to achieve the maximum value of sharing location-based information and achieve ‘national situational awareness’ for emergency managers, a mechanism to allow for the open exchange of information between organizations must be established. A national partnership program, GeoConnections, is workin g with the public safety and security community to evolve and expand the Canadian Geospatial Data Infrastructure (CGDI) to facilitate the open exchange of locationbased information. This paper will outline relevant programs and policies that facilitate information sharing, and will provide a case study of an inter-jurisdictional application that is improving horizontal, location-based information sharing.
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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 | 0.010 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.011 |
| Science and technology studies | 0.015 | 0.006 |
| Scholarly communication | 0.012 | 0.010 |
| Open science | 0.005 | 0.011 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".