Development of a historical ice database for the study of climate change in Canada
Bibliographic record
Abstract
Abstract The Canadian government has been compiling various observations on freshwater and coastal sea ice conditions for many years. However, the records are not easily accessible and are dispersed within different government departments. Given this, a major effort was undertaken in order to gather all available observations into a common database—the Canadian Ice Database (CID). This database will respond to the needs for climate monitoring in Canada, the validation and improvement of numerical ice models and the development of new remote‐sensing methods. Indeed, several studies have shown that freshwater ice and sea ice are good proxy indicators of climate variability and change. The first version of CID contains in situ observations from 757 sites distributed across Canada, which were originally kept on digital or paper records at the Meteorological Service of Canada Headquarters and the Canadian Ice Service (CIS). The CID holds 63 546 records covering the period from ice season 1822–23 to 2000–01. An analysis of the database allows one to trace the temporal evolution of the ice networks. The freeze‐up/break‐up network of 2000–01 only represents 4% of what it was in 1985–86. A drastic decline of the ice thickness and the snow on ice network is also observable. In 1997–98, it represented only 10% of the network that existed in 1984–85. The major budget cuts in Canadian government agencies during the late 1980s and the 1990s offer the most plausible explanation for the drastic decline in the ice observation networks. Weekly ice coverage determination on large lakes from satellite imagery by the CIS and the national volunteer ice monitoring program, IceWatch, may provide a means of reviving, at least, the freeze‐up/break‐up network. Copyright © 2002 John Wiley & Sons, Ltd.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.013 | 0.022 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".