WRS-Canada: Integration of the Landsat Worldwide Referencing
Bibliographic record
Abstract
Canadians are responsible for stewardship of approximately 10 % of the world’s forests. As a result, we as Canadians must be able to represent our forests in a manner which portrays a variety of economic, social, and environmental conditions. To meet these broad information challenges, the Canadian Forest Service of Natural Resources Canada, in partnership with the Canadian Space Agency, has begun to monitor Canada’s forests with space-based technology and Landsat data through a long-term project, the Earth Observation for Sustainable Development of Forests (EOSD). All Landsat satellite images of the earth’s surface are col-lected on a Worldwide Referencing System (WRS). WRS partitions the globe into overlapping frames representing the locations where Landsat data has, and will be, collected. The WRS is a useful tool for image selection and cataloging, organization and processing, and the development of sam-pling techniques. This technical transfer note outlines the information poten-tial of WRS-Canada when integrating information such as forest cover, elevation, and population. The authors describe a new on-line tool that allows Web users to determine which WRS frames in Canada correspond to various political, ecological, topographical, and demographic characteristics. Examples of queries that this system can handle might include: • Which Landsat frames contain areas that are over 500 metres in elevation? • Which Landsat frames in British Columbia contain over 100,000 people? • Which Landsat frames are more than 10 % wetland? • What proportion of forest cover can be expected by Landsat frame (figure 1)? This type of information should facilitate land-use plan-ning and decision-making for many stakeholders across the country.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.013 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 0.008 |
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".