Canadian Geospatial Data Infrastructure target vision
Bibliographic record
Abstract
Geospatial information underlies many of the facilities and services that we take for granted today. Everything from postal codes to weather maps is referenced to geographic location. A tour guide that describes museums but includes no road or address information is not very useful. A weather chart without a map as a backdrop is difficult to interpret. Demographic data without reference to location is of little value. Geospatial information today is pervasive and a core component of our society and our economy. It should not be strange to think of geospatial information as an infrastructure anymore than we think of highways, telecommunications, health care, air traffic control, and policing as infrastructures that we depend on and use daily. The concept of a Canadian Geospatial Data Infrastructure (CGDI) is born of this recognition. Geospatial infor mation is a significant subset of the information explosion that has occurred over the last decade. In the broadest sense, geospatial databases are databases that include information about the location (street address, latitude/longitude, section/township/range) of features in the databases. For many information technology applications, this locational information, or geographic reference, is a key component that facilitates the integration, analysis, and visualization of data. There is a wide and rapidly expanding range of information technology applications that rely on geospatial databases and their embedded geographic reference information. · An emergency response application is the ability to rapidly convert the telephone number of an incoming emergency 911 call to a map that shows the location of the caller and the most rapid avenue of response. · A public safety example is the ability to map a group of similar crimes or accidents to identify patterns that can assist in solving or preventing these incidents. · An environmental application is the use of soil maps, population counts, and road network information to make land use planning decisions. · An economic development application is the capability to bring together the information that a manufacturing plant developer requires in identifying and evaluating potential development sites. Such information might include: the location of potential properties, the availability and nature of transportation systems in the area, the nearby availability of qualified personnel, the proximity of available suppliers, and nature and the availability of utility infrastructure. For all of these applications, location is a critical part of the information because it allows the information to be brought together so that it can be analyzed and displayed. Each of these examples is commonplace today. But many applications are achieved only though tremendous effort and great expense because the underlying information infrastructure either does not yet exist or has not been developed consistently. GeoConnections is a national initiative comprised of seven programs, led by Natural Resources Canada. GeoConnections facilitates broad -based collaboration among federal, provincial, municipal governments as well as the private sector, that is fostering the development of the CGDI. It is anticipated that this infrastructure will, through its ease of use and demonstrable value, become a self-sustaining infrastructure like the Internet, and its many pieces will be supported by the commercial and government organizations that employ it. The CGDI will work on top of existing Internet technology. It will be that portion of the Internet related to the discovery, sharing and use of Canadian geospatial information and services. The CGDI is intended to provide Canadians with on-demand access to geospatial information, technologies and services through an inter-connected network of data, service and technology suppliers. It will advance the development of knowledge applications, decision support systems and commercial products that use geospatial data and technologies. CGDI will enable the sharing and use of geospatially referenced information. The basic ability to share and use information will lead to innovation and unforeseen applications that have broad social and economic value. For this reason, the development of CGDI will focus on the architecture and enabling technologies rather than on any specific applications. Organizations will use CGDI specifications to implement operational systems, and thus ensure their ability to share and use geospatial information and services. CGDI will succeed through those who use it and contribute to it. It has the potential to provide significant and lasting social, environmental, and economic benefit to the people of Canada.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.014 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.013 | 0.005 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.070 | 0.031 |
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".