The Canada Centre for Remote Sensing and the Canadian Astronaut Office Collaboration in the Space for Species Educational Program
Bibliographic record
Abstract
A partnership between the Canada Centre for Remote Sensing (CCRS), Natural Resources Canada, and the Canadian Astronaut Office (CAO), Canadian Space Agency, exists in order to promote Earth observation expertise from two unique perspectives; from satellites and from manned platforms. This paper focuses on one area of this effort, Space for Species (SFS), a Web-based learning program that promotes the monitoring of migratory species and their habitats from a perspective beyond the Earth's atmosphere. SFS is a co-operative effort involving the Canadian Space Agency (CSA), the Canadian Wildlife Service (CWS), the Canadian Wildlife Federation (CWF) and corporate sponsors. CCRS provides satellite imagery and research support to SFS. The program encourages students in grades six through nine to track the movements of four selected species at risk of extinction in Canada by observing the habitats of these species from space by using satellite imagery and astronaut photographs, monitoring daily and seasonal climatological conditions that affect species' movements and evaluating threats to species along migratory routes. The program also provides the opportunity for students to communicate with field biologists, remote sensing scientists and Canadian astronauts; all of whom will offer expertise, as well as, help students interpret collected data. Students also gain first-hand experience in developing species recovery plans. This paper describes the SFS learning program and highlights the Earth observation component and content of the program Web site.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.082 | 0.017 |
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".