Sea Ice in the Canadian Arctic in the 21st Century
Bibliographic record
Abstract
Numerical simulations of future climate by Global Climate Models, including that of the Meteorological Service of Canada, agree that climate warming will occur first and most intensely in Arctic regions. Observational evidence from recent decades indicates that the Arctic has been warming and that this warming is unprecedented over the past 400 years. The extent of Arctic sea ice, as observed mainly by satellite, has decreased at a rate of about 3% per decade since the 1970’s. Within Canadian Arctic waters, a similar rate of decrease has been observed over the period 1969-2000. Of potentially greater significance, estimates based on submarine measurements indicate that Arctic ice thickness has diminished by 40% over the past 40 years. If the recent rate of thinning were to be sustained, the Arctic Ocean could be essentially ice-free in summer as early as 2050. While there is considerable scientific debate about the timing and intensity of changes in the future state of Arctic sea ice, the predominant scientific opinion is that there will be much less ice in the Arctic in future than we have seen in the past. In a future with less sea ice, Canada could see much more marine transportation in its Arctic regions, related not only to the development of local natural resources but also to trans-shipment between Europe and Asia. This paper presents some recent results of climatological analysis of Canadian ice information and discusses probable scenarios for ice conditions in this century
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".