Earth Observation for Sustainable Development of Forests (EOSD): Project Overview. Intl. Geoscience and Remote Sensing Symp
Bibliographic record
Abstract
Abstract- Canada requires a next generation forest measuring and monitoring system that responds to key policy drivers related to climate change and to report upon sustainable forest development of Canada’s forest both nationally and internationally. The Canadian Forest Service, in partnership with the Canadian Space Agency, is using space-based earth observation (EO) technologies to create products for forest inventory, forest carbon accounting, monitoring sustainable development, and landscape management. The Earth Observation for Sustainable Development of Forests (EOSD) initiative will work in partnership with the Provinces and Territories and develop a land cover map of the forested area of Canada. Research programs are also a component of EOSD to develop techniques for change monitoring, biomass estimates and automated processing to aid in production. Inputs from EOSD will be an important data source in the National Forest Carbon Accounting Framework and will also be used to enhance Canada’s new plot-based National Forest Inventory. Initially EOSD, working with the provinces, territories, universities and industry, will work to develop a national map of the forested land cover of Canada with the long term goal of producing not only land cover maps, but maps of forest change over time, and biomass. The National Forest Information System will be used to integrate and synthesize applicable data and products and make them accessible to a wide range of users through the web.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.010 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".