Scientific basis for Ice Regime System: March 2000 update
Bibliographic record
Abstract
This report provides an update on the work being carried out by the Canadian Hydraulics Centre of the NRC, to put the Arctic Ice Regime Shipping System (AIRSS) on a scientific basis. Although the project is not complete, significant progress has been made towards its intended goal. This report is meant to highlight the progress, and to lead to a focused discussion in the marine community on the final form for the ice regime system. The process of putting the ice regime system on a scientific basis involves a systematic approach using empirical data in a pragmatic format. The report provides details of vessel damage, and shows the agreement of the current definition of the Ice Numeral with available full-scale data. It has been found that although the system reasonably well reflects the observations, there are numerous instances where the agreement is poor. An advanced scheme has been proposed which takes into account the “interaction” aspects of vessels in ice-covered waters. Using this approach, a significant improvement in the definition of the ice numeral can be achieved. This approach further “rewards” high ice class vessels with experienced Ice Navigators operating in a prudent manner, and still “penalizes” low ice class vessels, especially in the presence of multi-year ice.
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 | 0.011 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.012 | 0.010 |
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".