Variability and change in the Canadian cryosphere (snow and ice) - A Canadian contribution to "State and Fate of the Cryosphere"
Bibliographic record
Abstract
New satellite-derived observations of the cryosphere are being developed by Canadian scientists to contribute a snapshot of the current state of the cryosphere in northern Canada and to generate new information, data sets and monitoring capabilities for tundra and alpine snow cover, seasonal frozen ground, lake ice, albedo, land cover and phenology, snow melt characteristics over ice caps, sea ice fluxes through the Arctic islands, and river ice monitoring in northern Québec. Field campaigns are essential for these satellite retrieval activities, and to provide unique observations on characteristics of the cryosphere. Since April 2007, several field campaigns involving both ground-based surveys of snow cover, glaciers and ice caps, river ice and frozen ground characteristics and aircraft remote sensing have taken place across northern Canada (Yukon, NWT, northern Québec, Nunavut and Labrador). These field measurement data sets are an important Canadian contribution to the IPY ¿snapshot¿ by providing key information on the state of the cryosphere in northern Canada and an important baseline for assessing future changes. Many residents in northern Canada depend on frozen rivers and sea ice for transportation routes by snowmobile and sled in order to carry out traditional hunting and fishing activities. Outreach activities with northern communities are focussed on providing new information on current river ice and sea ice conditions in their local area to assist residents in planning safe navigation routes. The development of specialized river ice and sea ice floe edge map products based on satellite radar images has been achieved through the integration of science and traditional knowledge.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".