Bibliographic record
Abstract
Many engineering problems require an estimate of ice thickness, either the maximum likely thickness or the thickness at some time during the winter. This can be for estimating ice forces on an offshore structure in the sea or a bridge pier in a river. Operation of icebreaking ships requires knowledge of ice thickness to establish the viability of transit to northern ports. Similarly, over-ice transportation on seas, lakes or rives depends on a knowledge of ice thickness. At some locations historical records can be used to estimate ice thicknesses, but with changing climate historical records have limited applicability. Having means for predicting ice growth during a winter is a helpful tool. In high Arctic regions it is assumed the primary factors affecting ice growth are air temperature and snow depth. Assessment of the equations against data from Arctic weather stations indicates that the incorporation of snow depth in terms of a mean annual snow depth is a simple means for improving their predictive capability. Modified equations including mean annual snow depth and freezing degree days as input parameters are proposed and tested against available data at two locations in the Canadian High Arctic; Resolute Bay in a marine coastal environment and Baker lake in an inland freshwater lake. These modified prediction equations are proposed for general application in the Arctic.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".